Merge branch 'main' into FEATURE/Vision

CONTRIBUTING-ZH.md

@@ -123,6 +123,7 @@ Flowise 支持不同的环境变量来配置您的实例。您可以在 `package
 | PORT                        | Flowise 运行的 HTTP 端口                               | 数字                                            | 3000                                |
 | FLOWISE_USERNAME            | 登录用户名                                             | 字符串                                          |                                     |
 | FLOWISE_PASSWORD            | 登录密码                                               | 字符串                                          |                                     |
+| FLOWISE_FILE_SIZE_LIMIT     | 上传文件大小限制                                       | 字符串                                          | 50mb                                |
 | DEBUG                       | 打印组件的日志                                         | 布尔值                                          |                                     |
 | BLOB_STORAGE_PATH           | 存储位置                                               | 字符串                                          | `your-home-dir/.flowise/storage`    |
 | LOG_PATH                    | 存储日志文件的位置                                     | 字符串                                          | `your-path/Flowise/logs`            |

CONTRIBUTING.md

@@ -127,6 +127,7 @@ Flowise support different environment variables to configure your instance. You
 | IFRAME_ORIGINS              | The allowed origins for iframe src embedding                                 | String                                           |                                     |
 | FLOWISE_USERNAME            | Username to login                                                            | String                                           |                                     |
 | FLOWISE_PASSWORD            | Password to login                                                            | String                                           |                                     |
+| FLOWISE_FILE_SIZE_LIMIT     | Upload File Size Limit                                                       | String                                           | 50mb                                |
 | DEBUG                       | Print logs from components                                                   | Boolean                                          |                                     |
 | BLOB_STORAGE_PATH           | Location where uploaded files are stored                                     | String                                           | `your-home-dir/.flowise/storage`    |
 | LOG_PATH                    | Location where log files are stored                                          | String                                           | `your-path/Flowise/logs`            |

docker/.env.example

@@ -22,6 +22,7 @@ BLOB_STORAGE_PATH=/root/.flowise/storage
 # FLOWISE_USERNAME=user
 # FLOWISE_PASSWORD=1234
 # FLOWISE_SECRETKEY_OVERWRITE=myencryptionkey
+# FLOWISE_FILE_SIZE_LIMIT=50mb
 # DEBUG=true
 # LOG_LEVEL=debug (error | warn | info | verbose | debug)
 # TOOL_FUNCTION_BUILTIN_DEP=crypto,fs

docker/docker-compose.yml

@@ -10,6 +10,7 @@ services:
             - IFRAME_ORIGINS=${IFRAME_ORIGINS}
             - FLOWISE_USERNAME=${FLOWISE_USERNAME}
             - FLOWISE_PASSWORD=${FLOWISE_PASSWORD}
+            - FLOWISE_FILE_SIZE_LIMIT=${FLOWISE_FILE_SIZE_LIMIT}
             - DEBUG=${DEBUG}
             - DATABASE_PATH=${DATABASE_PATH}
             - DATABASE_TYPE=${DATABASE_TYPE}

package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise",
-    "version": "1.4.12",
+    "version": "1.5.0",
     "private": true,
     "homepage": "https://flowiseai.com",
     "workspaces": [

packages/components/nodes/chatmodels/AzureChatOpenAI/AzureChatOpenAI_LlamaIndex.ts

@@ -0,0 +1,135 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { OpenAI, ALL_AVAILABLE_OPENAI_MODELS } from 'llamaindex'
+
+interface AzureOpenAIConfig {
+    apiKey?: string
+    endpoint?: string
+    apiVersion?: string
+    deploymentName?: string
+}
+
+class AzureChatOpenAI_LlamaIndex_ChatModels implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    tags: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'AzureChatOpenAI'
+        this.name = 'azureChatOpenAI_LlamaIndex'
+        this.version = 1.0
+        this.type = 'AzureChatOpenAI'
+        this.icon = 'Azure.svg'
+        this.category = 'Chat Models'
+        this.description = 'Wrapper around Azure OpenAI Chat LLM specific for LlamaIndex'
+        this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(OpenAI)]
+        this.tags = ['LlamaIndex']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['azureOpenAIApi']
+        }
+        this.inputs = [
+            {
+                label: 'Model Name',
+                name: 'modelName',
+                type: 'options',
+                options: [
+                    {
+                        label: 'gpt-4',
+                        name: 'gpt-4'
+                    },
+                    {
+                        label: 'gpt-4-32k',
+                        name: 'gpt-4-32k'
+                    },
+                    {
+                        label: 'gpt-3.5-turbo',
+                        name: 'gpt-3.5-turbo'
+                    },
+                    {
+                        label: 'gpt-3.5-turbo-16k',
+                        name: 'gpt-3.5-turbo-16k'
+                    }
+                ],
+                default: 'gpt-3.5-turbo-16k',
+                optional: true
+            },
+            {
+                label: 'Temperature',
+                name: 'temperature',
+                type: 'number',
+                step: 0.1,
+                default: 0.9,
+                optional: true
+            },
+            {
+                label: 'Max Tokens',
+                name: 'maxTokens',
+                type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top Probability',
+                name: 'topP',
+                type: 'number',
+                step: 0.1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Timeout',
+                name: 'timeout',
+                type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const modelName = nodeData.inputs?.modelName as keyof typeof ALL_AVAILABLE_OPENAI_MODELS
+        const temperature = nodeData.inputs?.temperature as string
+        const maxTokens = nodeData.inputs?.maxTokens as string
+        const topP = nodeData.inputs?.topP as string
+        const timeout = nodeData.inputs?.timeout as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const azureOpenAIApiKey = getCredentialParam('azureOpenAIApiKey', credentialData, nodeData)
+        const azureOpenAIApiInstanceName = getCredentialParam('azureOpenAIApiInstanceName', credentialData, nodeData)
+        const azureOpenAIApiDeploymentName = getCredentialParam('azureOpenAIApiDeploymentName', credentialData, nodeData)
+        const azureOpenAIApiVersion = getCredentialParam('azureOpenAIApiVersion', credentialData, nodeData)
+
+        const obj: Partial<OpenAI> & { azure?: AzureOpenAIConfig } = {
+            temperature: parseFloat(temperature),
+            model: modelName,
+            azure: {
+                apiKey: azureOpenAIApiKey,
+                endpoint: `https://${azureOpenAIApiInstanceName}.openai.azure.com`,
+                apiVersion: azureOpenAIApiVersion,
+                deploymentName: azureOpenAIApiDeploymentName
+            }
+        }
+
+        if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
+        if (topP) obj.topP = parseFloat(topP)
+        if (timeout) obj.timeout = parseInt(timeout, 10)
+
+        const model = new OpenAI(obj)
+        return model
+    }
+}
+
+module.exports = { nodeClass: AzureChatOpenAI_LlamaIndex_ChatModels }

packages/components/nodes/chatmodels/ChatAnthropic/ChatAnthropic_LlamaIndex.ts

@@ -0,0 +1,104 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { Anthropic } from 'llamaindex'
+
+class ChatAnthropic_LlamaIndex_ChatModels implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    tags: string[]
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'ChatAnthropic'
+        this.name = 'chatAnthropic_LlamaIndex'
+        this.version = 1.0
+        this.type = 'ChatAnthropic'
+        this.icon = 'Anthropic.svg'
+        this.category = 'Chat Models'
+        this.description = 'Wrapper around ChatAnthropic LLM specific for LlamaIndex'
+        this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(Anthropic)]
+        this.tags = ['LlamaIndex']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['anthropicApi']
+        }
+        this.inputs = [
+            {
+                label: 'Model Name',
+                name: 'modelName',
+                type: 'options',
+                options: [
+                    {
+                        label: 'claude-2',
+                        name: 'claude-2',
+                        description: 'Claude 2 latest major version, automatically get updates to the model as they are released'
+                    },
+                    {
+                        label: 'claude-instant-1',
+                        name: 'claude-instant-1',
+                        description: 'Claude Instant latest major version, automatically get updates to the model as they are released'
+                    }
+                ],
+                default: 'claude-2',
+                optional: true
+            },
+            {
+                label: 'Temperature',
+                name: 'temperature',
+                type: 'number',
+                step: 0.1,
+                default: 0.9,
+                optional: true
+            },
+            {
+                label: 'Max Tokens',
+                name: 'maxTokensToSample',
+                type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top P',
+                name: 'topP',
+                type: 'number',
+                step: 0.1,
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const temperature = nodeData.inputs?.temperature as string
+        const modelName = nodeData.inputs?.modelName as 'claude-2' | 'claude-instant-1' | undefined
+        const maxTokensToSample = nodeData.inputs?.maxTokensToSample as string
+        const topP = nodeData.inputs?.topP as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const anthropicApiKey = getCredentialParam('anthropicApiKey', credentialData, nodeData)
+
+        const obj: Partial<Anthropic> = {
+            temperature: parseFloat(temperature),
+            model: modelName,
+            apiKey: anthropicApiKey
+        }
+
+        if (maxTokensToSample) obj.maxTokens = parseInt(maxTokensToSample, 10)
+        if (topP) obj.topP = parseFloat(topP)
+
+        const model = new Anthropic(obj)
+        return model
+    }
+}
+
+module.exports = { nodeClass: ChatAnthropic_LlamaIndex_ChatModels }

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

@@ -0,0 +1,156 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { OpenAI, ALL_AVAILABLE_OPENAI_MODELS } from 'llamaindex'
+
+class ChatOpenAI_LlamaIndex_LLMs implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    tags: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'ChatOpenAI'
+        this.name = 'chatOpenAI_LlamaIndex'
+        this.version = 1.0
+        this.type = 'ChatOpenAI'
+        this.icon = 'openai.svg'
+        this.category = 'Chat Models'
+        this.description = 'Wrapper around OpenAI Chat LLM specific for LlamaIndex'
+        this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(OpenAI)]
+        this.tags = ['LlamaIndex']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['openAIApi']
+        }
+        this.inputs = [
+            {
+                label: 'Model Name',
+                name: 'modelName',
+                type: 'options',
+                options: [
+                    {
+                        label: 'gpt-4',
+                        name: 'gpt-4'
+                    },
+                    {
+                        label: 'gpt-4-turbo-preview',
+                        name: 'gpt-4-turbo-preview'
+                    },
+                    {
+                        label: 'gpt-4-0125-preview',
+                        name: 'gpt-4-0125-preview'
+                    },
+                    {
+                        label: 'gpt-4-1106-preview',
+                        name: 'gpt-4-1106-preview'
+                    },
+                    {
+                        label: 'gpt-4-vision-preview',
+                        name: 'gpt-4-vision-preview'
+                    },
+                    {
+                        label: 'gpt-4-0613',
+                        name: 'gpt-4-0613'
+                    },
+                    {
+                        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
+            },
+            {
+                label: 'Temperature',
+                name: 'temperature',
+                type: 'number',
+                step: 0.1,
+                default: 0.9,
+                optional: true
+            },
+            {
+                label: 'Max Tokens',
+                name: 'maxTokens',
+                type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top Probability',
+                name: 'topP',
+                type: 'number',
+                step: 0.1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Timeout',
+                name: 'timeout',
+                type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const temperature = nodeData.inputs?.temperature as string
+        const modelName = nodeData.inputs?.modelName as keyof typeof ALL_AVAILABLE_OPENAI_MODELS
+        const maxTokens = nodeData.inputs?.maxTokens as string
+        const topP = nodeData.inputs?.topP as string
+        const timeout = nodeData.inputs?.timeout as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
+
+        const obj: Partial<OpenAI> = {
+            temperature: parseFloat(temperature),
+            model: modelName,
+            apiKey: openAIApiKey
+        }
+
+        if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
+        if (topP) obj.topP = parseFloat(topP)
+        if (timeout) obj.timeout = parseInt(timeout, 10)
+
+        const model = new OpenAI(obj)
+        return model
+    }
+}
+
+module.exports = { nodeClass: ChatOpenAI_LlamaIndex_LLMs }

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

@@ -126,7 +126,9 @@ class Cheerio_DocumentLoaders implements INode {
         let docs = []
         if (relativeLinksMethod) {
             if (process.env.DEBUG === 'true') options.logger.info(`Start ${relativeLinksMethod}`)
-            if (!limit) limit = 10
+            // if limit is 0 we don't want it to default to 10 so we check explicitly for null or undefined
+            // so when limit is 0 we can fetch all the links
+            if (limit === null || limit === undefined) limit = 10
             else if (limit < 0) throw new Error('Limit cannot be less than 0')
             const pages: string[] =
                 selectedLinks && selectedLinks.length > 0
@@ -143,7 +145,7 @@ class Cheerio_DocumentLoaders implements INode {
         } else if (selectedLinks && selectedLinks.length > 0) {
             if (process.env.DEBUG === 'true')
                 options.logger.info(`pages: ${JSON.stringify(selectedLinks)}, length: ${selectedLinks.length}`)
-            for (const page of selectedLinks) {
+            for (const page of selectedLinks.slice(0, limit)) {
                 docs.push(...(await cheerioLoader(page)))
             }
         } else {

packages/components/nodes/documentloaders/Folder/Folder.ts

@@ -34,6 +34,12 @@ class Folder_DocumentLoaders implements INode {
                 type: 'string',
                 placeholder: ''
             },
+            {
+                label: 'Recursive',
+                name: 'recursive',
+                type: 'boolean',
+                additionalParams: false
+            },
             {
                 label: 'Text Splitter',
                 name: 'textSplitter',
@@ -54,48 +60,54 @@ class Folder_DocumentLoaders implements INode {
         const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
         const folderPath = nodeData.inputs?.folderPath as string
         const metadata = nodeData.inputs?.metadata
+        const recursive = nodeData.inputs?.recursive as boolean
 
-        const loader = new DirectoryLoader(folderPath, {
-            '.json': (path) => new JSONLoader(path),
-            '.txt': (path) => new TextLoader(path),
-            '.csv': (path) => new CSVLoader(path),
-            '.docx': (path) => new DocxLoader(path),
-            // @ts-ignore
-            '.pdf': (path) => new PDFLoader(path, { pdfjs: () => import('pdf-parse/lib/pdf.js/v1.10.100/build/pdf.js') }),
-            '.aspx': (path) => new TextLoader(path),
-            '.asp': (path) => new TextLoader(path),
-            '.cpp': (path) => new TextLoader(path), // C++
-            '.c': (path) => new TextLoader(path),
-            '.cs': (path) => new TextLoader(path),
-            '.css': (path) => new TextLoader(path),
-            '.go': (path) => new TextLoader(path), // Go
-            '.h': (path) => new TextLoader(path), // C++ Header files
-            '.java': (path) => new TextLoader(path), // Java
-            '.js': (path) => new TextLoader(path), // JavaScript
-            '.less': (path) => new TextLoader(path), // Less files
-            '.ts': (path) => new TextLoader(path), // TypeScript
-            '.php': (path) => new TextLoader(path), // PHP
-            '.proto': (path) => new TextLoader(path), // Protocol Buffers
-            '.python': (path) => new TextLoader(path), // Python
-            '.py': (path) => new TextLoader(path), // Python
-            '.rst': (path) => new TextLoader(path), // reStructuredText
-            '.ruby': (path) => new TextLoader(path), // Ruby
-            '.rb': (path) => new TextLoader(path), // Ruby
-            '.rs': (path) => new TextLoader(path), // Rust
-            '.scala': (path) => new TextLoader(path), // Scala
-            '.sc': (path) => new TextLoader(path), // Scala
-            '.scss': (path) => new TextLoader(path), // Sass
-            '.sol': (path) => new TextLoader(path), // Solidity
-            '.sql': (path) => new TextLoader(path), //SQL
-            '.swift': (path) => new TextLoader(path), // Swift
-            '.markdown': (path) => new TextLoader(path), // Markdown
-            '.md': (path) => new TextLoader(path), // Markdown
-            '.tex': (path) => new TextLoader(path), // LaTeX
-            '.ltx': (path) => new TextLoader(path), // LaTeX
-            '.html': (path) => new TextLoader(path), // HTML
-            '.vb': (path) => new TextLoader(path), // Visual Basic
-            '.xml': (path) => new TextLoader(path) // XML
-        })
+        const loader = new DirectoryLoader(
+            folderPath,
+            {
+                '.json': (path) => new JSONLoader(path),
+                '.txt': (path) => new TextLoader(path),
+                '.csv': (path) => new CSVLoader(path),
+                '.docx': (path) => new DocxLoader(path),
+                // @ts-ignore
+                '.pdf': (path) => new PDFLoader(path, { pdfjs: () => import('pdf-parse/lib/pdf.js/v1.10.100/build/pdf.js') }),
+                '.aspx': (path) => new TextLoader(path),
+                '.asp': (path) => new TextLoader(path),
+                '.cpp': (path) => new TextLoader(path), // C++
+                '.c': (path) => new TextLoader(path),
+                '.cs': (path) => new TextLoader(path),
+                '.css': (path) => new TextLoader(path),
+                '.go': (path) => new TextLoader(path), // Go
+                '.h': (path) => new TextLoader(path), // C++ Header files
+                '.kt': (path) => new TextLoader(path), // Kotlin
+                '.java': (path) => new TextLoader(path), // Java
+                '.js': (path) => new TextLoader(path), // JavaScript
+                '.less': (path) => new TextLoader(path), // Less files
+                '.ts': (path) => new TextLoader(path), // TypeScript
+                '.php': (path) => new TextLoader(path), // PHP
+                '.proto': (path) => new TextLoader(path), // Protocol Buffers
+                '.python': (path) => new TextLoader(path), // Python
+                '.py': (path) => new TextLoader(path), // Python
+                '.rst': (path) => new TextLoader(path), // reStructuredText
+                '.ruby': (path) => new TextLoader(path), // Ruby
+                '.rb': (path) => new TextLoader(path), // Ruby
+                '.rs': (path) => new TextLoader(path), // Rust
+                '.scala': (path) => new TextLoader(path), // Scala
+                '.sc': (path) => new TextLoader(path), // Scala
+                '.scss': (path) => new TextLoader(path), // Sass
+                '.sol': (path) => new TextLoader(path), // Solidity
+                '.sql': (path) => new TextLoader(path), //SQL
+                '.swift': (path) => new TextLoader(path), // Swift
+                '.markdown': (path) => new TextLoader(path), // Markdown
+                '.md': (path) => new TextLoader(path), // Markdown
+                '.tex': (path) => new TextLoader(path), // LaTeX
+                '.ltx': (path) => new TextLoader(path), // LaTeX
+                '.html': (path) => new TextLoader(path), // HTML
+                '.vb': (path) => new TextLoader(path), // Visual Basic
+                '.xml': (path) => new TextLoader(path) // XML
+            },
+            recursive
+        )
         let docs = []
 
         if (textSplitter) {

packages/components/nodes/documentloaders/PlainText/PlainText.ts

@@ -51,11 +51,13 @@ class PlainText_DocumentLoaders implements INode {
             {
                 label: 'Document',
                 name: 'document',
-                baseClasses: this.baseClasses
+                description: 'Array of document objects containing metadata and pageContent',
+                baseClasses: [...this.baseClasses, 'json']
             },
             {
                 label: 'Text',
                 name: 'text',
+                description: 'Concatenated string from pageContent of documents',
                 baseClasses: ['string', 'json']
             }
         ]

packages/components/nodes/documentloaders/Playwright/Playwright.ts

@@ -167,7 +167,9 @@ class Playwright_DocumentLoaders implements INode {
         let docs = []
         if (relativeLinksMethod) {
             if (process.env.DEBUG === 'true') options.logger.info(`Start ${relativeLinksMethod}`)
-            if (!limit) limit = 10
+            // if limit is 0 we don't want it to default to 10 so we check explicitly for null or undefined
+            // so when limit is 0 we can fetch all the links
+            if (limit === null || limit === undefined) limit = 10
             else if (limit < 0) throw new Error('Limit cannot be less than 0')
             const pages: string[] =
                 selectedLinks && selectedLinks.length > 0
@@ -184,7 +186,7 @@ class Playwright_DocumentLoaders implements INode {
         } else if (selectedLinks && selectedLinks.length > 0) {
             if (process.env.DEBUG === 'true')
                 options.logger.info(`pages: ${JSON.stringify(selectedLinks)}, length: ${selectedLinks.length}`)
-            for (const page of selectedLinks) {
+            for (const page of selectedLinks.slice(0, limit)) {
                 docs.push(...(await playwrightLoader(page)))
             }
         } else {

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

@@ -168,7 +168,9 @@ class Puppeteer_DocumentLoaders implements INode {
         let docs = []
         if (relativeLinksMethod) {
             if (process.env.DEBUG === 'true') options.logger.info(`Start ${relativeLinksMethod}`)
-            if (!limit) limit = 10
+            // if limit is 0 we don't want it to default to 10 so we check explicitly for null or undefined
+            // so when limit is 0 we can fetch all the links
+            if (limit === null || limit === undefined) limit = 10
             else if (limit < 0) throw new Error('Limit cannot be less than 0')
             const pages: string[] =
                 selectedLinks && selectedLinks.length > 0
@@ -185,7 +187,7 @@ class Puppeteer_DocumentLoaders implements INode {
         } else if (selectedLinks && selectedLinks.length > 0) {
             if (process.env.DEBUG === 'true')
                 options.logger.info(`pages: ${JSON.stringify(selectedLinks)}, length: ${selectedLinks.length}`)
-            for (const page of selectedLinks) {
+            for (const page of selectedLinks.slice(0, limit)) {
                 docs.push(...(await puppeteerLoader(page)))
             }
         } else {

packages/components/nodes/documentloaders/Text/Text.ts

@@ -51,11 +51,13 @@ class Text_DocumentLoaders implements INode {
             {
                 label: 'Document',
                 name: 'document',
-                baseClasses: this.baseClasses
+                description: 'Array of document objects containing metadata and pageContent',
+                baseClasses: [...this.baseClasses, 'json']
             },
             {
                 label: 'Text',
                 name: 'text',
+                description: 'Concatenated string from pageContent of documents',
                 baseClasses: ['string', 'json']
             }
         ]

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

@@ -51,11 +51,13 @@ class VectorStoreToDocument_DocumentLoaders implements INode {
             {
                 label: 'Document',
                 name: 'document',
+                description: 'Array of document objects containing metadata and pageContent',
                 baseClasses: [...this.baseClasses, 'json']
             },
             {
                 label: 'Text',
                 name: 'text',
+                description: 'Concatenated string from pageContent of documents',
                 baseClasses: ['string', 'json']
             }
         ]

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

@@ -0,0 +1,77 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { OpenAIEmbedding } from 'llamaindex'
+
+interface AzureOpenAIConfig {
+    apiKey?: string
+    endpoint?: string
+    apiVersion?: string
+    deploymentName?: string
+}
+
+class AzureOpenAIEmbedding_LlamaIndex_Embeddings implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    credential: INodeParams
+    tags: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Azure OpenAI Embeddings'
+        this.name = 'azureOpenAIEmbeddingsLlamaIndex'
+        this.version = 1.0
+        this.type = 'AzureOpenAIEmbeddings'
+        this.icon = 'Azure.svg'
+        this.category = 'Embeddings'
+        this.description = 'Azure OpenAI API embeddings specific for LlamaIndex'
+        this.baseClasses = [this.type, 'BaseEmbedding_LlamaIndex', ...getBaseClasses(OpenAIEmbedding)]
+        this.tags = ['LlamaIndex']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['azureOpenAIApi']
+        }
+        this.inputs = [
+            {
+                label: 'Timeout',
+                name: 'timeout',
+                type: 'number',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const timeout = nodeData.inputs?.timeout as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const azureOpenAIApiKey = getCredentialParam('azureOpenAIApiKey', credentialData, nodeData)
+        const azureOpenAIApiInstanceName = getCredentialParam('azureOpenAIApiInstanceName', credentialData, nodeData)
+        const azureOpenAIApiDeploymentName = getCredentialParam('azureOpenAIApiDeploymentName', credentialData, nodeData)
+        const azureOpenAIApiVersion = getCredentialParam('azureOpenAIApiVersion', credentialData, nodeData)
+
+        const obj: Partial<OpenAIEmbedding> & { azure?: AzureOpenAIConfig } = {
+            azure: {
+                apiKey: azureOpenAIApiKey,
+                endpoint: `https://${azureOpenAIApiInstanceName}.openai.azure.com`,
+                apiVersion: azureOpenAIApiVersion,
+                deploymentName: azureOpenAIApiDeploymentName
+            }
+        }
+
+        if (timeout) obj.timeout = parseInt(timeout, 10)
+
+        const model = new OpenAIEmbedding(obj)
+        return model
+    }
+}
+
+module.exports = { nodeClass: AzureOpenAIEmbedding_LlamaIndex_Embeddings }

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

@@ -0,0 +1,91 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { OpenAIEmbedding } from 'llamaindex'
+
+class OpenAIEmbedding_LlamaIndex_Embeddings implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    tags: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'OpenAI Embedding'
+        this.name = 'openAIEmbedding_LlamaIndex'
+        this.version = 1.0
+        this.type = 'OpenAIEmbedding'
+        this.icon = 'openai.svg'
+        this.category = 'Embeddings'
+        this.description = 'OpenAI Embedding specific for LlamaIndex'
+        this.baseClasses = [this.type, 'BaseEmbedding_LlamaIndex', ...getBaseClasses(OpenAIEmbedding)]
+        this.tags = ['LlamaIndex']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['openAIApi']
+        }
+        this.inputs = [
+            {
+                label: 'Model Name',
+                name: 'modelName',
+                type: 'options',
+                options: [
+                    {
+                        label: 'text-embedding-3-large',
+                        name: 'text-embedding-3-large'
+                    },
+                    {
+                        label: 'text-embedding-3-small',
+                        name: 'text-embedding-3-small'
+                    },
+                    {
+                        label: 'text-embedding-ada-002',
+                        name: 'text-embedding-ada-002'
+                    }
+                ],
+                default: 'text-embedding-ada-002',
+                optional: true
+            },
+            {
+                label: 'Timeout',
+                name: 'timeout',
+                type: 'number',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'BasePath',
+                name: 'basepath',
+                type: 'string',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const timeout = nodeData.inputs?.timeout as string
+        const modelName = nodeData.inputs?.modelName as string
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
+
+        const obj: Partial<OpenAIEmbedding> = {
+            apiKey: openAIApiKey,
+            model: modelName
+        }
+        if (timeout) obj.timeout = parseInt(timeout, 10)
+
+        const model = new OpenAIEmbedding(obj)
+        return model
+    }
+}
+
+module.exports = { nodeClass: OpenAIEmbedding_LlamaIndex_Embeddings }

packages/components/nodes/engine/ChatEngine/ContextChatEngine.ts

@@ -0,0 +1,149 @@
+import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { BaseNode, Metadata, BaseRetriever, LLM, ContextChatEngine, ChatMessage } from 'llamaindex'
+import { reformatSourceDocuments } from '../EngineUtils'
+
+class ContextChatEngine_LlamaIndex implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    tags: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+    sessionId?: string
+
+    constructor(fields?: { sessionId?: string }) {
+        this.label = 'Context Chat Engine'
+        this.name = 'contextChatEngine'
+        this.version = 1.0
+        this.type = 'ContextChatEngine'
+        this.icon = 'context-chat-engine.png'
+        this.category = 'Engine'
+        this.description = 'Answer question based on retrieved documents (context) with built-in memory to remember conversation'
+        this.baseClasses = [this.type]
+        this.tags = ['LlamaIndex']
+        this.inputs = [
+            {
+                label: 'Chat Model',
+                name: 'model',
+                type: 'BaseChatModel_LlamaIndex'
+            },
+            {
+                label: 'Vector Store Retriever',
+                name: 'vectorStoreRetriever',
+                type: 'VectorIndexRetriever'
+            },
+            {
+                label: 'Memory',
+                name: 'memory',
+                type: 'BaseChatMemory'
+            },
+            {
+                label: 'Return Source Documents',
+                name: 'returnSourceDocuments',
+                type: 'boolean',
+                optional: true
+            },
+            {
+                label: 'System Message',
+                name: 'systemMessagePrompt',
+                type: 'string',
+                rows: 4,
+                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.'
+            }
+        ]
+        this.sessionId = fields?.sessionId
+    }
+
+    async init(): Promise<any> {
+        return null
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
+        const model = nodeData.inputs?.model as LLM
+        const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
+        const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
+        const memory = nodeData.inputs?.memory as FlowiseMemory
+        const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
+
+        const chatHistory = [] as ChatMessage[]
+
+        if (systemMessagePrompt) {
+            chatHistory.push({
+                content: systemMessagePrompt,
+                role: 'user'
+            })
+        }
+
+        const chatEngine = new ContextChatEngine({ chatModel: model, retriever: vectorStoreRetriever })
+
+        const msgs = (await memory.getChatMessages(this.sessionId, false, options.chatHistory)) as IMessage[]
+        for (const message of msgs) {
+            if (message.type === 'apiMessage') {
+                chatHistory.push({
+                    content: message.message,
+                    role: 'assistant'
+                })
+            } else if (message.type === 'userMessage') {
+                chatHistory.push({
+                    content: message.message,
+                    role: 'user'
+                })
+            }
+        }
+
+        let text = ''
+        let isStreamingStarted = false
+        let sourceDocuments: ICommonObject[] = []
+        let sourceNodes: BaseNode<Metadata>[] = []
+        const isStreamingEnabled = options.socketIO && options.socketIOClientId
+
+        if (isStreamingEnabled) {
+            const stream = await chatEngine.chat({ message: input, chatHistory, stream: true })
+            for await (const chunk of stream) {
+                text += chunk.response
+                if (chunk.sourceNodes) sourceNodes = chunk.sourceNodes
+                if (!isStreamingStarted) {
+                    isStreamingStarted = true
+                    options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
+                }
+
+                options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
+            }
+
+            if (returnSourceDocuments) {
+                sourceDocuments = reformatSourceDocuments(sourceNodes)
+                options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
+            }
+        } else {
+            const response = await chatEngine.chat({ message: input, chatHistory })
+            text = response?.response
+            sourceDocuments = reformatSourceDocuments(response?.sourceNodes ?? [])
+        }
+
+        await memory.addChatMessages(
+            [
+                {
+                    text: input,
+                    type: 'userMessage'
+                },
+                {
+                    text: text,
+                    type: 'apiMessage'
+                }
+            ],
+            this.sessionId
+        )
+
+        if (returnSourceDocuments) return { text, sourceDocuments }
+        else return { text }
+    }
+}
+
+module.exports = { nodeClass: ContextChatEngine_LlamaIndex }

packages/components/nodes/engine/ChatEngine/SimpleChatEngine.ts

@@ -0,0 +1,124 @@
+import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { LLM, ChatMessage, SimpleChatEngine } from 'llamaindex'
+
+class SimpleChatEngine_LlamaIndex implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    tags: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+    sessionId?: string
+
+    constructor(fields?: { sessionId?: string }) {
+        this.label = 'Simple Chat Engine'
+        this.name = 'simpleChatEngine'
+        this.version = 1.0
+        this.type = 'SimpleChatEngine'
+        this.icon = 'chat-engine.png'
+        this.category = 'Engine'
+        this.description = 'Simple engine to handle back and forth conversations'
+        this.baseClasses = [this.type]
+        this.tags = ['LlamaIndex']
+        this.inputs = [
+            {
+                label: 'Chat Model',
+                name: 'model',
+                type: 'BaseChatModel_LlamaIndex'
+            },
+            {
+                label: 'Memory',
+                name: 'memory',
+                type: 'BaseChatMemory'
+            },
+            {
+                label: 'System Message',
+                name: 'systemMessagePrompt',
+                type: 'string',
+                rows: 4,
+                optional: true,
+                placeholder: 'You are a helpful assistant'
+            }
+        ]
+        this.sessionId = fields?.sessionId
+    }
+
+    async init(): Promise<any> {
+        return null
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const model = nodeData.inputs?.model as LLM
+        const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
+        const memory = nodeData.inputs?.memory as FlowiseMemory
+
+        const chatHistory = [] as ChatMessage[]
+
+        if (systemMessagePrompt) {
+            chatHistory.push({
+                content: systemMessagePrompt,
+                role: 'user'
+            })
+        }
+
+        const chatEngine = new SimpleChatEngine({ llm: model })
+
+        const msgs = (await memory.getChatMessages(this.sessionId, false, options.chatHistory)) as IMessage[]
+        for (const message of msgs) {
+            if (message.type === 'apiMessage') {
+                chatHistory.push({
+                    content: message.message,
+                    role: 'assistant'
+                })
+            } else if (message.type === 'userMessage') {
+                chatHistory.push({
+                    content: message.message,
+                    role: 'user'
+                })
+            }
+        }
+
+        let text = ''
+        let isStreamingStarted = false
+        const isStreamingEnabled = options.socketIO && options.socketIOClientId
+
+        if (isStreamingEnabled) {
+            const stream = await chatEngine.chat({ message: input, chatHistory, stream: true })
+            for await (const chunk of stream) {
+                text += chunk.response
+                if (!isStreamingStarted) {
+                    isStreamingStarted = true
+                    options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
+                }
+
+                options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
+            }
+        } else {
+            const response = await chatEngine.chat({ message: input, chatHistory })
+            text = response?.response
+        }
+
+        await memory.addChatMessages(
+            [
+                {
+                    text: input,
+                    type: 'userMessage'
+                },
+                {
+                    text: text,
+                    type: 'apiMessage'
+                }
+            ],
+            this.sessionId
+        )
+
+        return text
+    }
+}
+
+module.exports = { nodeClass: SimpleChatEngine_LlamaIndex }

packages/components/nodes/engine/EngineUtils.ts

@@ -0,0 +1,12 @@
+import { BaseNode, Metadata } from 'llamaindex'
+
+export const reformatSourceDocuments = (sourceNodes: BaseNode<Metadata>[]) => {
+    const sourceDocuments = []
+    for (const node of sourceNodes) {
+        sourceDocuments.push({
+            pageContent: (node as any).text,
+            metadata: node.metadata
+        })
+    }
+    return sourceDocuments
+}

packages/components/nodes/engine/QueryEngine/QueryEngine.ts

@@ -0,0 +1,143 @@
+import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import {
+    RetrieverQueryEngine,
+    ResponseSynthesizer,
+    CompactAndRefine,
+    TreeSummarize,
+    Refine,
+    SimpleResponseBuilder,
+    BaseNode,
+    Metadata
+} from 'llamaindex'
+import { reformatSourceDocuments } from '../EngineUtils'
+
+class QueryEngine_LlamaIndex implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    tags: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+    sessionId?: string
+
+    constructor(fields?: { sessionId?: string }) {
+        this.label = 'Query Engine'
+        this.name = 'queryEngine'
+        this.version = 1.0
+        this.type = 'QueryEngine'
+        this.icon = 'query-engine.png'
+        this.category = 'Engine'
+        this.description = 'Simple query engine built to answer question over your data, without memory'
+        this.baseClasses = [this.type]
+        this.tags = ['LlamaIndex']
+        this.inputs = [
+            {
+                label: 'Vector Store Retriever',
+                name: 'vectorStoreRetriever',
+                type: 'VectorIndexRetriever'
+            },
+            {
+                label: 'Response Synthesizer',
+                name: 'responseSynthesizer',
+                type: 'ResponseSynthesizer',
+                description:
+                    'ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. See <a target="_blank" href="https://ts.llamaindex.ai/modules/low_level/response_synthesizer">more</a>',
+                optional: true
+            },
+            {
+                label: 'Return Source Documents',
+                name: 'returnSourceDocuments',
+                type: 'boolean',
+                optional: true
+            }
+        ]
+        this.sessionId = fields?.sessionId
+    }
+
+    async init(): Promise<any> {
+        return null
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
+        const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
+        const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever
+        const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
+
+        let queryEngine = new RetrieverQueryEngine(vectorStoreRetriever)
+
+        if (responseSynthesizerObj) {
+            if (responseSynthesizerObj.type === 'TreeSummarize') {
+                const responseSynthesizer = new ResponseSynthesizer({
+                    responseBuilder: new TreeSummarize(vectorStoreRetriever.serviceContext, responseSynthesizerObj.textQAPromptTemplate),
+                    serviceContext: vectorStoreRetriever.serviceContext
+                })
+                queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
+            } else if (responseSynthesizerObj.type === 'CompactAndRefine') {
+                const responseSynthesizer = new ResponseSynthesizer({
+                    responseBuilder: new CompactAndRefine(
+                        vectorStoreRetriever.serviceContext,
+                        responseSynthesizerObj.textQAPromptTemplate,
+                        responseSynthesizerObj.refinePromptTemplate
+                    ),
+                    serviceContext: vectorStoreRetriever.serviceContext
+                })
+                queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
+            } else if (responseSynthesizerObj.type === 'Refine') {
+                const responseSynthesizer = new ResponseSynthesizer({
+                    responseBuilder: new Refine(
+                        vectorStoreRetriever.serviceContext,
+                        responseSynthesizerObj.textQAPromptTemplate,
+                        responseSynthesizerObj.refinePromptTemplate
+                    ),
+                    serviceContext: vectorStoreRetriever.serviceContext
+                })
+                queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
+            } else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
+                const responseSynthesizer = new ResponseSynthesizer({
+                    responseBuilder: new SimpleResponseBuilder(vectorStoreRetriever.serviceContext),
+                    serviceContext: vectorStoreRetriever.serviceContext
+                })
+                queryEngine = new RetrieverQueryEngine(vectorStoreRetriever, responseSynthesizer)
+            }
+        }
+
+        let text = ''
+        let sourceDocuments: ICommonObject[] = []
+        let sourceNodes: BaseNode<Metadata>[] = []
+        let isStreamingStarted = false
+        const isStreamingEnabled = options.socketIO && options.socketIOClientId
+
+        if (isStreamingEnabled) {
+            const stream = await queryEngine.query({ query: input, stream: true })
+            for await (const chunk of stream) {
+                text += chunk.response
+                if (chunk.sourceNodes) sourceNodes = chunk.sourceNodes
+                if (!isStreamingStarted) {
+                    isStreamingStarted = true
+                    options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
+                }
+
+                options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
+            }
+
+            if (returnSourceDocuments) {
+                sourceDocuments = reformatSourceDocuments(sourceNodes)
+                options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
+            }
+        } else {
+            const response = await queryEngine.query({ query: input })
+            text = response?.response
+            sourceDocuments = reformatSourceDocuments(response?.sourceNodes ?? [])
+        }
+
+        if (returnSourceDocuments) return { text, sourceDocuments }
+        else return { text }
+    }
+}
+
+module.exports = { nodeClass: QueryEngine_LlamaIndex }

packages/components/nodes/engine/SubQuestionQueryEngine/SubQuestionQueryEngine.ts

@@ -0,0 +1,193 @@
+import { flatten } from 'lodash'
+import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import {
+    TreeSummarize,
+    SimpleResponseBuilder,
+    Refine,
+    BaseEmbedding,
+    ResponseSynthesizer,
+    CompactAndRefine,
+    QueryEngineTool,
+    LLMQuestionGenerator,
+    SubQuestionQueryEngine,
+    BaseNode,
+    Metadata,
+    serviceContextFromDefaults
+} from 'llamaindex'
+import { reformatSourceDocuments } from '../EngineUtils'
+
+class SubQuestionQueryEngine_LlamaIndex implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    tags: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+    sessionId?: string
+
+    constructor(fields?: { sessionId?: string }) {
+        this.label = 'Sub Question Query Engine'
+        this.name = 'subQuestionQueryEngine'
+        this.version = 1.0
+        this.type = 'SubQuestionQueryEngine'
+        this.icon = 'subQueryEngine.svg'
+        this.category = 'Engine'
+        this.description =
+            'Breaks complex query into sub questions for each relevant data source, then gather all the intermediate reponses and synthesizes a final response'
+        this.baseClasses = [this.type]
+        this.tags = ['LlamaIndex']
+        this.inputs = [
+            {
+                label: 'QueryEngine Tools',
+                name: 'queryEngineTools',
+                type: 'QueryEngineTool',
+                list: true
+            },
+            {
+                label: 'Chat Model',
+                name: 'model',
+                type: 'BaseChatModel_LlamaIndex'
+            },
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'BaseEmbedding_LlamaIndex'
+            },
+            {
+                label: 'Response Synthesizer',
+                name: 'responseSynthesizer',
+                type: 'ResponseSynthesizer',
+                description:
+                    'ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. See <a target="_blank" href="https://ts.llamaindex.ai/modules/low_level/response_synthesizer">more</a>',
+                optional: true
+            },
+            {
+                label: 'Return Source Documents',
+                name: 'returnSourceDocuments',
+                type: 'boolean',
+                optional: true
+            }
+        ]
+        this.sessionId = fields?.sessionId
+    }
+
+    async init(): Promise<any> {
+        return null
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
+        const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
+        const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
+        const model = nodeData.inputs?.model
+
+        const serviceContext = serviceContextFromDefaults({
+            llm: model,
+            embedModel: embeddings
+        })
+
+        let queryEngineTools = nodeData.inputs?.queryEngineTools as QueryEngineTool[]
+        queryEngineTools = flatten(queryEngineTools)
+
+        let queryEngine = SubQuestionQueryEngine.fromDefaults({
+            serviceContext,
+            queryEngineTools,
+            questionGen: new LLMQuestionGenerator({ llm: model })
+        })
+
+        const responseSynthesizerObj = nodeData.inputs?.responseSynthesizer
+        if (responseSynthesizerObj) {
+            if (responseSynthesizerObj.type === 'TreeSummarize') {
+                const responseSynthesizer = new ResponseSynthesizer({
+                    responseBuilder: new TreeSummarize(serviceContext, responseSynthesizerObj.textQAPromptTemplate),
+                    serviceContext
+                })
+                queryEngine = SubQuestionQueryEngine.fromDefaults({
+                    responseSynthesizer,
+                    serviceContext,
+                    queryEngineTools,
+                    questionGen: new LLMQuestionGenerator({ llm: model })
+                })
+            } else if (responseSynthesizerObj.type === 'CompactAndRefine') {
+                const responseSynthesizer = new ResponseSynthesizer({
+                    responseBuilder: new CompactAndRefine(
+                        serviceContext,
+                        responseSynthesizerObj.textQAPromptTemplate,
+                        responseSynthesizerObj.refinePromptTemplate
+                    ),
+                    serviceContext
+                })
+                queryEngine = SubQuestionQueryEngine.fromDefaults({
+                    responseSynthesizer,
+                    serviceContext,
+                    queryEngineTools,
+                    questionGen: new LLMQuestionGenerator({ llm: model })
+                })
+            } else if (responseSynthesizerObj.type === 'Refine') {
+                const responseSynthesizer = new ResponseSynthesizer({
+                    responseBuilder: new Refine(
+                        serviceContext,
+                        responseSynthesizerObj.textQAPromptTemplate,
+                        responseSynthesizerObj.refinePromptTemplate
+                    ),
+                    serviceContext
+                })
+                queryEngine = SubQuestionQueryEngine.fromDefaults({
+                    responseSynthesizer,
+                    serviceContext,
+                    queryEngineTools,
+                    questionGen: new LLMQuestionGenerator({ llm: model })
+                })
+            } else if (responseSynthesizerObj.type === 'SimpleResponseBuilder') {
+                const responseSynthesizer = new ResponseSynthesizer({
+                    responseBuilder: new SimpleResponseBuilder(serviceContext),
+                    serviceContext
+                })
+                queryEngine = SubQuestionQueryEngine.fromDefaults({
+                    responseSynthesizer,
+                    serviceContext,
+                    queryEngineTools,
+                    questionGen: new LLMQuestionGenerator({ llm: model })
+                })
+            }
+        }
+
+        let text = ''
+        let sourceDocuments: ICommonObject[] = []
+        let sourceNodes: BaseNode<Metadata>[] = []
+        let isStreamingStarted = false
+        const isStreamingEnabled = options.socketIO && options.socketIOClientId
+
+        if (isStreamingEnabled) {
+            const stream = await queryEngine.query({ query: input, stream: true })
+            for await (const chunk of stream) {
+                text += chunk.response
+                if (chunk.sourceNodes) sourceNodes = chunk.sourceNodes
+                if (!isStreamingStarted) {
+                    isStreamingStarted = true
+                    options.socketIO.to(options.socketIOClientId).emit('start', chunk.response)
+                }
+
+                options.socketIO.to(options.socketIOClientId).emit('token', chunk.response)
+            }
+
+            if (returnSourceDocuments) {
+                sourceDocuments = reformatSourceDocuments(sourceNodes)
+                options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
+            }
+        } else {
+            const response = await queryEngine.query({ query: input })
+            text = response?.response
+            sourceDocuments = reformatSourceDocuments(response?.sourceNodes ?? [])
+        }
+
+        if (returnSourceDocuments) return { text, sourceDocuments }
+        else return { text }
+    }
+}
+
+module.exports = { nodeClass: SubQuestionQueryEngine_LlamaIndex }

packages/components/nodes/engine/SubQuestionQueryEngine/subQueryEngine.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-filter-question" 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="M15 19l-6 2v-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 4.414" /><path d="M19 22v.01" /><path d="M19 19a2.003 2.003 0 0 0 .914 -3.782a1.98 1.98 0 0 0 -2.414 .483" /></svg>

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

@@ -117,7 +117,10 @@ const initalizeDynamoDB = async (nodeData: INodeData, options: ICommonObject): P
         memoryKey: memoryKey ?? 'chat_history',
         chatHistory: dynamoDb,
         sessionId,
-        dynamodbClient: client
+        dynamodbClient: client,
+        tableName,
+        partitionKey,
+        dynamoKey: { [partitionKey]: { S: sessionId } }
     })
     return memory
 }
@@ -125,6 +128,9 @@ const initalizeDynamoDB = async (nodeData: INodeData, options: ICommonObject): P
 interface BufferMemoryExtendedInput {
     dynamodbClient: DynamoDBClient
     sessionId: string
+    tableName: string
+    partitionKey: string
+    dynamoKey: Record<string, AttributeValue>
 }
 
 interface DynamoDBSerializedChatMessage {
@@ -142,6 +148,10 @@ interface DynamoDBSerializedChatMessage {
 }
 
 class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
+    private tableName = ''
+    private partitionKey = ''
+    private dynamoKey: Record<string, AttributeValue>
+    private messageAttributeName: string
     sessionId = ''
     dynamodbClient: DynamoDBClient
 
@@ -149,11 +159,14 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
         super(fields)
         this.sessionId = fields.sessionId
         this.dynamodbClient = fields.dynamodbClient
+        this.tableName = fields.tableName
+        this.partitionKey = fields.partitionKey
+        this.dynamoKey = fields.dynamoKey
     }
 
     overrideDynamoKey(overrideSessionId = '') {
-        const existingDynamoKey = (this as any).dynamoKey
-        const partitionKey = (this as any).partitionKey
+        const existingDynamoKey = this.dynamoKey
+        const partitionKey = this.partitionKey
 
         let newDynamoKey: Record<string, AttributeValue> = {}
 
@@ -209,9 +222,9 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async getChatMessages(overrideSessionId = '', returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
         if (!this.dynamodbClient) return []
 
-        const dynamoKey = overrideSessionId ? this.overrideDynamoKey(overrideSessionId) : (this as any).dynamoKey
-        const tableName = (this as any).tableName
-        const messageAttributeName = (this as any).messageAttributeName
+        const dynamoKey = overrideSessionId ? this.overrideDynamoKey(overrideSessionId) : this.dynamoKey
+        const tableName = this.tableName
+        const messageAttributeName = this.messageAttributeName
 
         const params: GetItemCommandInput = {
             TableName: tableName,
@@ -236,9 +249,9 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId = ''): Promise<void> {
         if (!this.dynamodbClient) return
 
-        const dynamoKey = overrideSessionId ? this.overrideDynamoKey(overrideSessionId) : (this as any).dynamoKey
-        const tableName = (this as any).tableName
-        const messageAttributeName = (this as any).messageAttributeName
+        const dynamoKey = overrideSessionId ? this.overrideDynamoKey(overrideSessionId) : this.dynamoKey
+        const tableName = this.tableName
+        const messageAttributeName = this.messageAttributeName
 
         const input = msgArray.find((msg) => msg.type === 'userMessage')
         const output = msgArray.find((msg) => msg.type === 'apiMessage')
@@ -259,8 +272,8 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async clearChatMessages(overrideSessionId = ''): Promise<void> {
         if (!this.dynamodbClient) return
 
-        const dynamoKey = overrideSessionId ? this.overrideDynamoKey(overrideSessionId) : (this as any).dynamoKey
-        const tableName = (this as any).tableName
+        const dynamoKey = overrideSessionId ? this.overrideDynamoKey(overrideSessionId) : this.dynamoKey
+        const tableName = this.tableName
 
         const params: DeleteItemCommandInput = {
             TableName: tableName,

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

@@ -154,7 +154,7 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async getChatMessages(overrideSessionId = '', returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
         if (!this.collection) return []
 
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         const document = await this.collection.findOne({ sessionId: id })
         const messages = document?.messages || []
         const baseMessages = messages.map(mapStoredMessageToChatMessage)
@@ -164,7 +164,7 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId = ''): Promise<void> {
         if (!this.collection) return
 
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         const input = msgArray.find((msg) => msg.type === 'userMessage')
         const output = msgArray.find((msg) => msg.type === 'apiMessage')
 
@@ -196,7 +196,7 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async clearChatMessages(overrideSessionId = ''): Promise<void> {
         if (!this.collection) return
 
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         await this.collection.deleteOne({ sessionId: id })
         await this.clear()
     }

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

@@ -141,7 +141,7 @@ class MotorheadMemoryExtended extends MotorheadMemory implements MemoryMethods {
     }
 
     async getChatMessages(overrideSessionId = '', returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         try {
             const resp = await this.caller.call(fetch, `${this.url}/sessions/${id}/memory`, {
                 //@ts-ignore
@@ -172,7 +172,7 @@ class MotorheadMemoryExtended extends MotorheadMemory implements MemoryMethods {
     }
 
     async addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId = ''): Promise<void> {
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         const input = msgArray.find((msg) => msg.type === 'userMessage')
         const output = msgArray.find((msg) => msg.type === 'apiMessage')
         const inputValues = { [this.inputKey ?? 'input']: input?.text }
@@ -182,7 +182,7 @@ class MotorheadMemoryExtended extends MotorheadMemory implements MemoryMethods {
     }
 
     async clearChatMessages(overrideSessionId = ''): Promise<void> {
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         await this.clear(id)
     }
 }

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

@@ -189,7 +189,7 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async getChatMessages(overrideSessionId = '', returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
         if (!this.redisClient) return []
 
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         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)
@@ -199,7 +199,7 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId = ''): Promise<void> {
         if (!this.redisClient) return
 
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         const input = msgArray.find((msg) => msg.type === 'userMessage')
         const output = msgArray.find((msg) => msg.type === 'apiMessage')
 
@@ -219,7 +219,7 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async clearChatMessages(overrideSessionId = ''): Promise<void> {
         if (!this.redisClient) return
 
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         await this.redisClient.del(id)
         await this.clear()
     }

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

@@ -114,7 +114,7 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async getChatMessages(overrideSessionId = '', returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
         if (!this.redisClient) return []
 
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         const rawStoredMessages: StoredMessage[] = await this.redisClient.lrange<StoredMessage>(id, 0, -1)
         const orderedMessages = rawStoredMessages.reverse()
         const previousMessages = orderedMessages.filter((x): x is StoredMessage => x.type !== undefined && x.data.content !== undefined)
@@ -125,7 +125,7 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId = ''): Promise<void> {
         if (!this.redisClient) return
 
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         const input = msgArray.find((msg) => msg.type === 'userMessage')
         const output = msgArray.find((msg) => msg.type === 'apiMessage')
 
@@ -145,7 +145,7 @@ class BufferMemoryExtended extends FlowiseMemory implements MemoryMethods {
     async clearChatMessages(overrideSessionId = ''): Promise<void> {
         if (!this.redisClient) return
 
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         await this.redisClient.del(id)
         await this.clear()
     }

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

@@ -163,14 +163,14 @@ class ZepMemoryExtended extends ZepMemory implements MemoryMethods {
     }
 
     async getChatMessages(overrideSessionId = '', returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         const memoryVariables = await this.loadMemoryVariables({}, id)
         const baseMessages = memoryVariables[this.memoryKey]
         return returnBaseMessages ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
     }
 
     async addChatMessages(msgArray: { text: string; type: MessageType }[], overrideSessionId = ''): Promise<void> {
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         const input = msgArray.find((msg) => msg.type === 'userMessage')
         const output = msgArray.find((msg) => msg.type === 'apiMessage')
         const inputValues = { [this.inputKey ?? 'input']: input?.text }
@@ -180,7 +180,7 @@ class ZepMemoryExtended extends ZepMemory implements MemoryMethods {
     }
 
     async clearChatMessages(overrideSessionId = ''): Promise<void> {
-        const id = overrideSessionId ?? this.sessionId
+        const id = overrideSessionId ? overrideSessionId : this.sessionId
         await this.clear(id)
     }
 }

packages/components/nodes/outputparsers/StructuredOutputParserAdvanced/StructuredOutputParserAdvanced.ts

@@ -0,0 +1,79 @@
+import { getBaseClasses, INode, INodeData, INodeParams } from '../../../src'
+import { BaseOutputParser } from 'langchain/schema/output_parser'
+import { StructuredOutputParser as LangchainStructuredOutputParser } from 'langchain/output_parsers'
+import { CATEGORY } from '../OutputParserHelpers'
+import { z } from 'zod'
+
+class AdvancedStructuredOutputParser implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+
+    constructor() {
+        this.label = 'Advanced Structured Output Parser'
+        this.name = 'advancedStructuredOutputParser'
+        this.version = 1.0
+        this.type = 'AdvancedStructuredOutputParser'
+        this.description = 'Parse the output of an LLM call into a given structure by providing a Zod schema.'
+        this.icon = 'structure.svg'
+        this.category = CATEGORY
+        this.baseClasses = [this.type, ...getBaseClasses(BaseOutputParser)]
+        this.inputs = [
+            {
+                label: 'Autofix',
+                name: 'autofixParser',
+                type: 'boolean',
+                optional: true,
+                description: 'In the event that the first call fails, will make another call to the model to fix any errors.'
+            },
+            {
+                label: 'Example JSON',
+                name: 'exampleJson',
+                type: 'string',
+                description: 'Zod schema for the output of the model',
+                rows: 10,
+                default: `z.object({
+    title: z.string(), // Title of the movie as a string
+    yearOfRelease: z.number().int(), // Release year as an integer number,
+    genres: z.enum([
+        "Action", "Comedy", "Drama", "Fantasy", "Horror",
+        "Mystery", "Romance", "Science Fiction", "Thriller", "Documentary"
+    ]).array().max(2), // Array of genres, max of 2 from the defined enum
+    shortDescription: z.string().max(500) // Short description, max 500 characters
+})`
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const schemaString = nodeData.inputs?.exampleJson as string
+        const autoFix = nodeData.inputs?.autofixParser as boolean
+
+        const zodSchemaFunction = new Function('z', `return ${schemaString}`)
+        const zodSchema = zodSchemaFunction(z)
+
+        try {
+            const structuredOutputParser = LangchainStructuredOutputParser.fromZodSchema(zodSchema)
+
+            // NOTE: When we change Flowise to return a json response, the following has to be changed to: JsonStructuredOutputParser
+            Object.defineProperty(structuredOutputParser, 'autoFix', {
+                enumerable: true,
+                configurable: true,
+                writable: true,
+                value: autoFix
+            })
+            return structuredOutputParser
+        } catch (exception) {
+            throw new Error('Error parsing Zod Schema: ' + exception)
+        }
+    }
+}
+
+module.exports = { nodeClass: AdvancedStructuredOutputParser }

packages/components/nodes/outputparsers/StructuredOutputParserAdvanced/structure.svg

@@ -0,0 +1,8 @@
+<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M16 3V13M16 3L13 6.13609M16 3L19 6.13609" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M7 15V12C7 10.8954 7.89543 10 9 10H11M25 15V12C25 10.8954 24.1046 10 23 10H21" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M12.5644 20.4399C11.6769 19.7608 9 19.6332 9 21.7961C9 24.1915 13 22.5657 13 25.0902C13 26.9875 10.33 27.5912 9 26.3537" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M24 27V20L28 27V20" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M16 23.5C16 20.7 17.6667 20 18.5 20C19.3333 20 21 20.7 21 23.5C21 26.3 19.3333 27 18.5 27C17.6667 27 16 26.3 16 23.5Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M6 20V25C6 26.1046 5.10457 27 4 27V27C2.89543 27 2 26.1046 2 25V25" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>

packages/components/nodes/responsesynthesizer/CompactRefine/CompactRefine.ts

@@ -0,0 +1,75 @@
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { ResponseSynthesizerClass } from '../base'
+
+class CompactRefine_LlamaIndex implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    tags: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Compact and Refine'
+        this.name = 'compactrefineLlamaIndex'
+        this.version = 1.0
+        this.type = 'CompactRefine'
+        this.icon = 'compactrefine.svg'
+        this.category = 'Response Synthesizer'
+        this.description =
+            'CompactRefine is a slight variation of Refine that first compacts the text chunks into the smallest possible number of chunks.'
+        this.baseClasses = [this.type, 'ResponseSynthesizer']
+        this.tags = ['LlamaIndex']
+        this.inputs = [
+            {
+                label: 'Refine Prompt',
+                name: 'refinePrompt',
+                type: 'string',
+                rows: 4,
+                default: `The original query is as follows: {query}
+We have provided an existing answer: {existingAnswer}
+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 query. If the context isn't useful, return the original answer.
+Refined Answer:`,
+                warning: `Prompt can contains no variables, or up to 3 variables. Variables must be {existingAnswer}, {context} and {query}`,
+                optional: true
+            },
+            {
+                label: 'Text QA Prompt',
+                name: 'textQAPrompt',
+                type: 'string',
+                rows: 4,
+                default: `Context information is below.
+---------------------
+{context}
+---------------------
+Given the context information and not prior knowledge, answer the query.
+Query: {query}
+Answer:`,
+                warning: `Prompt can contains no variables, or up to 2 variables. Variables must be {context} and {query}`,
+                optional: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const refinePrompt = nodeData.inputs?.refinePrompt as string
+        const textQAPrompt = nodeData.inputs?.textQAPrompt as string
+
+        const refinePromptTemplate = ({ context = '', existingAnswer = '', query = '' }) =>
+            refinePrompt.replace('{existingAnswer}', existingAnswer).replace('{context}', context).replace('{query}', query)
+        const textQAPromptTemplate = ({ context = '', query = '' }) => textQAPrompt.replace('{context}', context).replace('{query}', query)
+
+        return new ResponseSynthesizerClass({ textQAPromptTemplate, refinePromptTemplate, type: 'CompactAndRefine' })
+    }
+}
+
+module.exports = { nodeClass: CompactRefine_LlamaIndex }

packages/components/nodes/responsesynthesizer/CompactRefine/compactrefine.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-layers-difference" 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="M16 16v2a2 2 0 0 1 -2 2h-8a2 2 0 0 1 -2 -2v-8a2 2 0 0 1 2 -2h2v-2a2 2 0 0 1 2 -2h8a2 2 0 0 1 2 2v8a2 2 0 0 1 -2 2h-2" /><path d="M10 8l-2 0l0 2" /><path d="M8 14l0 2l2 0" /><path d="M14 8l2 0l0 2" /><path d="M16 14l0 2l-2 0" /></svg>

packages/components/nodes/responsesynthesizer/Refine/Refine.ts

@@ -0,0 +1,75 @@
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { ResponseSynthesizerClass } from '../base'
+
+class Refine_LlamaIndex implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    tags: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Refine'
+        this.name = 'refineLlamaIndex'
+        this.version = 1.0
+        this.type = 'Refine'
+        this.icon = 'refine.svg'
+        this.category = 'Response Synthesizer'
+        this.description =
+            'Create and refine an answer by sequentially going through each retrieved text chunk. This makes a separate LLM call per Node. Good for more detailed answers.'
+        this.baseClasses = [this.type, 'ResponseSynthesizer']
+        this.tags = ['LlamaIndex']
+        this.inputs = [
+            {
+                label: 'Refine Prompt',
+                name: 'refinePrompt',
+                type: 'string',
+                rows: 4,
+                default: `The original query is as follows: {query}
+We have provided an existing answer: {existingAnswer}
+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 query. If the context isn't useful, return the original answer.
+Refined Answer:`,
+                warning: `Prompt can contains no variables, or up to 3 variables. Variables must be {existingAnswer}, {context} and {query}`,
+                optional: true
+            },
+            {
+                label: 'Text QA Prompt',
+                name: 'textQAPrompt',
+                type: 'string',
+                rows: 4,
+                default: `Context information is below.
+---------------------
+{context}
+---------------------
+Given the context information and not prior knowledge, answer the query.
+Query: {query}
+Answer:`,
+                warning: `Prompt can contains no variables, or up to 2 variables. Variables must be {context} and {query}`,
+                optional: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const refinePrompt = nodeData.inputs?.refinePrompt as string
+        const textQAPrompt = nodeData.inputs?.textQAPrompt as string
+
+        const refinePromptTemplate = ({ context = '', existingAnswer = '', query = '' }) =>
+            refinePrompt.replace('{existingAnswer}', existingAnswer).replace('{context}', context).replace('{query}', query)
+        const textQAPromptTemplate = ({ context = '', query = '' }) => textQAPrompt.replace('{context}', context).replace('{query}', query)
+
+        return new ResponseSynthesizerClass({ textQAPromptTemplate, refinePromptTemplate, type: 'Refine' })
+    }
+}
+
+module.exports = { nodeClass: Refine_LlamaIndex }

packages/components/nodes/responsesynthesizer/Refine/refine.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-filter-search" 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="M11.36 20.213l-2.36 .787v-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 4.414" /><path d="M18 18m-3 0a3 3 0 1 0 6 0a3 3 0 1 0 -6 0" /><path d="M20.2 20.2l1.8 1.8" /></svg>

packages/components/nodes/responsesynthesizer/SimpleResponseBuilder/SimpleResponseBuilder.ts

@@ -0,0 +1,35 @@
+import { INode, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { ResponseSynthesizerClass } from '../base'
+
+class SimpleResponseBuilder_LlamaIndex implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    tags: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Simple Response Builder'
+        this.name = 'simpleResponseBuilderLlamaIndex'
+        this.version = 1.0
+        this.type = 'SimpleResponseBuilder'
+        this.icon = 'simplerb.svg'
+        this.category = 'Response Synthesizer'
+        this.description = `Apply a query to a collection of text chunks, gathering the responses in an array, and return a combined string of all responses. Useful for individual queries on each text chunk.`
+        this.baseClasses = [this.type, 'ResponseSynthesizer']
+        this.tags = ['LlamaIndex']
+        this.inputs = []
+    }
+
+    async init(): Promise<any> {
+        return new ResponseSynthesizerClass({ type: 'SimpleResponseBuilder' })
+    }
+}
+
+module.exports = { nodeClass: SimpleResponseBuilder_LlamaIndex }

packages/components/nodes/responsesynthesizer/SimpleResponseBuilder/simplerb.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-quote" 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="M10 11h-4a1 1 0 0 1 -1 -1v-3a1 1 0 0 1 1 -1h3a1 1 0 0 1 1 1v6c0 2.667 -1.333 4.333 -4 5" /><path d="M19 11h-4a1 1 0 0 1 -1 -1v-3a1 1 0 0 1 1 -1h3a1 1 0 0 1 1 1v6c0 2.667 -1.333 4.333 -4 5" /></svg>

packages/components/nodes/responsesynthesizer/TreeSummarize/TreeSummarize.ts

@@ -0,0 +1,56 @@
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { ResponseSynthesizerClass } from '../base'
+
+class TreeSummarize_LlamaIndex implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    tags: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'TreeSummarize'
+        this.name = 'treeSummarizeLlamaIndex'
+        this.version = 1.0
+        this.type = 'TreeSummarize'
+        this.icon = 'treesummarize.svg'
+        this.category = 'Response Synthesizer'
+        this.description =
+            'Given a set of text chunks and the query, recursively construct a tree and return the root node as the response. Good for summarization purposes.'
+        this.baseClasses = [this.type, 'ResponseSynthesizer']
+        this.tags = ['LlamaIndex']
+        this.inputs = [
+            {
+                label: 'Prompt',
+                name: 'prompt',
+                type: 'string',
+                rows: 4,
+                default: `Context information from multiple sources is below.
+---------------------
+{context}
+---------------------
+Given the information from multiple sources and not prior knowledge, answer the query.
+Query: {query}
+Answer:`,
+                warning: `Prompt can contains no variables, or up to 2 variables. Variables must be {context} and {query}`,
+                optional: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const prompt = nodeData.inputs?.prompt as string
+
+        const textQAPromptTemplate = ({ context = '', query = '' }) => prompt.replace('{context}', context).replace('{query}', query)
+
+        return new ResponseSynthesizerClass({ textQAPromptTemplate, type: 'TreeSummarize' })
+    }
+}
+
+module.exports = { nodeClass: TreeSummarize_LlamaIndex }

packages/components/nodes/responsesynthesizer/TreeSummarize/treesummarize.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-tree" 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="M12 13l-2 -2" /><path d="M12 12l2 -2" /><path d="M12 21v-13" /><path d="M9.824 16a3 3 0 0 1 -2.743 -3.69a3 3 0 0 1 .304 -4.833a3 3 0 0 1 4.615 -3.707a3 3 0 0 1 4.614 3.707a3 3 0 0 1 .305 4.833a3 3 0 0 1 -2.919 3.695h-4z" /></svg>

packages/components/nodes/responsesynthesizer/base.ts

@@ -0,0 +1,11 @@
+export class ResponseSynthesizerClass {
+    type: string
+    textQAPromptTemplate?: any
+    refinePromptTemplate?: any
+
+    constructor(params: { type: string; textQAPromptTemplate?: any; refinePromptTemplate?: any }) {
+        this.type = params.type
+        this.textQAPromptTemplate = params.textQAPromptTemplate
+        this.refinePromptTemplate = params.refinePromptTemplate
+    }
+}

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

@@ -94,11 +94,13 @@ class CohereRerankRetriever_Retrievers implements INode {
             {
                 label: 'Document',
                 name: 'document',
-                baseClasses: ['Document']
+                description: 'Array of document objects containing metadata and pageContent',
+                baseClasses: ['Document', 'json']
             },
             {
                 label: 'Text',
                 name: 'text',
+                description: 'Concatenated string from pageContent of documents',
                 baseClasses: ['string', 'json']
             }
         ]

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

@@ -78,11 +78,13 @@ class EmbeddingsFilterRetriever_Retrievers implements INode {
             {
                 label: 'Document',
                 name: 'document',
-                baseClasses: ['Document']
+                description: 'Array of document objects containing metadata and pageContent',
+                baseClasses: ['Document', 'json']
             },
             {
                 label: 'Text',
                 name: 'text',
+                description: 'Concatenated string from pageContent of documents',
                 baseClasses: ['string', 'json']
             }
         ]

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

@@ -140,11 +140,13 @@ Passage:`
             {
                 label: 'Document',
                 name: 'document',
-                baseClasses: ['Document']
+                description: 'Array of document objects containing metadata and pageContent',
+                baseClasses: ['Document', 'json']
             },
             {
                 label: 'Text',
                 name: 'text',
+                description: 'Concatenated string from pageContent of documents',
                 baseClasses: ['string', 'json']
             }
         ]

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

@@ -58,11 +58,13 @@ class LLMFilterCompressionRetriever_Retrievers implements INode {
             {
                 label: 'Document',
                 name: 'document',
-                baseClasses: ['Document']
+                description: 'Array of document objects containing metadata and pageContent',
+                baseClasses: ['Document', 'json']
             },
             {
                 label: 'Text',
                 name: 'text',
+                description: 'Concatenated string from pageContent of documents',
                 baseClasses: ['string', 'json']
             }
         ]

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

@@ -89,11 +89,13 @@ class RRFRetriever_Retrievers implements INode {
             {
                 label: 'Document',
                 name: 'document',
-                baseClasses: ['Document']
+                description: 'Array of document objects containing metadata and pageContent',
+                baseClasses: ['Document', 'json']
             },
             {
                 label: 'Text',
                 name: 'text',
+                description: 'Concatenated string from pageContent of documents',
                 baseClasses: ['string', 'json']
             }
         ]

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

@@ -74,11 +74,13 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
             {
                 label: 'Document',
                 name: 'document',
-                baseClasses: ['Document']
+                description: 'Array of document objects containing metadata and pageContent',
+                baseClasses: ['Document', 'json']
             },
             {
                 label: 'Text',
                 name: 'text',
+                description: 'Concatenated string from pageContent of documents',
                 baseClasses: ['string', 'json']
             }
         ]

packages/components/nodes/tools/QueryEngineTool/QueryEngineTool.ts

@@ -0,0 +1,68 @@
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { VectorStoreIndex } from 'llamaindex'
+
+class QueryEngine_Tools implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    tags: string[]
+    baseClasses: string[]
+    inputs?: INodeParams[]
+
+    constructor() {
+        this.label = 'QueryEngine Tool'
+        this.name = 'queryEngineToolLlamaIndex'
+        this.version = 1.0
+        this.type = 'QueryEngineTool'
+        this.icon = 'queryEngineTool.svg'
+        this.category = 'Tools'
+        this.tags = ['LlamaIndex']
+        this.description = 'Tool used to invoke query engine'
+        this.baseClasses = [this.type]
+        this.inputs = [
+            {
+                label: 'Vector Store Index',
+                name: 'vectorStoreIndex',
+                type: 'VectorStoreIndex'
+            },
+            {
+                label: 'Tool Name',
+                name: 'toolName',
+                type: 'string',
+                description: 'Tool name must be small capital letter with underscore. Ex: my_tool'
+            },
+            {
+                label: 'Tool Description',
+                name: 'toolDesc',
+                type: 'string',
+                rows: 4
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const vectorStoreIndex = nodeData.inputs?.vectorStoreIndex as VectorStoreIndex
+        const toolName = nodeData.inputs?.toolName as string
+        const toolDesc = nodeData.inputs?.toolDesc as string
+        const queryEngineTool = {
+            queryEngine: vectorStoreIndex.asQueryEngine({
+                preFilters: {
+                    ...(vectorStoreIndex as any).metadatafilter
+                }
+            }),
+            metadata: {
+                name: toolName,
+                description: toolDesc
+            },
+            vectorStoreIndex
+        }
+
+        return queryEngineTool
+    }
+}
+
+module.exports = { nodeClass: QueryEngine_Tools }

packages/components/nodes/tools/QueryEngineTool/queryEngineTool.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-brand-google-big-query" 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="M17.73 19.875a2.225 2.225 0 0 1 -1.948 1.125h-7.283a2.222 2.222 0 0 1 -1.947 -1.158l-4.272 -6.75a2.269 2.269 0 0 1 0 -2.184l4.272 -6.75a2.225 2.225 0 0 1 1.946 -1.158h7.285c.809 0 1.554 .443 1.947 1.158l3.98 6.75a2.33 2.33 0 0 1 0 2.25l-3.98 6.75v-.033z" /><path d="M11.5 11.5m-3.5 0a3.5 3.5 0 1 0 7 0a3.5 3.5 0 1 0 -7 0" /><path d="M14 14l2 2" /></svg>

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

@@ -0,0 +1,383 @@
+import {
+    BaseNode,
+    Document,
+    Metadata,
+    VectorStore,
+    VectorStoreQuery,
+    VectorStoreQueryResult,
+    serviceContextFromDefaults,
+    storageContextFromDefaults,
+    VectorStoreIndex,
+    BaseEmbedding
+} from 'llamaindex'
+import { FetchResponse, Index, Pinecone, ScoredPineconeRecord } from '@pinecone-database/pinecone'
+import { flatten } from 'lodash'
+import { Document as LCDocument } from 'langchain/document'
+import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { flattenObject, getCredentialData, getCredentialParam } from '../../../src/utils'
+
+class PineconeLlamaIndex_VectorStores implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    tags: string[]
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Pinecone'
+        this.name = 'pineconeLlamaIndex'
+        this.version = 1.0
+        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.baseClasses = [this.type, 'VectorIndexRetriever']
+        this.tags = ['LlamaIndex']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['pineconeApi']
+        }
+        this.inputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                type: 'Document',
+                list: true,
+                optional: true
+            },
+            {
+                label: 'Chat Model',
+                name: 'model',
+                type: 'BaseChatModel_LlamaIndex'
+            },
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'BaseEmbedding_LlamaIndex'
+            },
+            {
+                label: 'Pinecone Index',
+                name: 'pineconeIndex',
+                type: 'string'
+            },
+            {
+                label: 'Pinecone Namespace',
+                name: 'pineconeNamespace',
+                type: 'string',
+                placeholder: 'my-first-namespace',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'Pinecone Metadata Filter',
+                name: 'pineconeMetadataFilter',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to 4',
+                placeholder: '4',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Pinecone Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Pinecone Vector Store Index',
+                name: 'vectorStore',
+                baseClasses: [this.type, 'VectorStoreIndex']
+            }
+        ]
+    }
+
+    //@ts-ignore
+    vectorStoreMethods = {
+        async upsert(nodeData: INodeData, options: ICommonObject): Promise<void> {
+            const indexName = nodeData.inputs?.pineconeIndex as string
+            const pineconeNamespace = nodeData.inputs?.pineconeNamespace as string
+            const docs = nodeData.inputs?.document as LCDocument[]
+            const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
+            const model = nodeData.inputs?.model
+
+            const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+            const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
+
+            const pcvs = new PineconeVectorStore({
+                indexName,
+                apiKey: pineconeApiKey,
+                namespace: pineconeNamespace
+            })
+
+            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 LCDocument(flattenDocs[i]))
+                }
+            }
+
+            const llamadocs: Document[] = []
+            for (const doc of finalDocs) {
+                llamadocs.push(new Document({ text: doc.pageContent, metadata: doc.metadata }))
+            }
+
+            const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
+            const storageContext = await storageContextFromDefaults({ vectorStore: pcvs })
+
+            try {
+                await VectorStoreIndex.fromDocuments(llamadocs, { serviceContext, storageContext })
+            } catch (e) {
+                throw new Error(e)
+            }
+        }
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const indexName = nodeData.inputs?.pineconeIndex as string
+        const pineconeNamespace = nodeData.inputs?.pineconeNamespace as string
+        const pineconeMetadataFilter = nodeData.inputs?.pineconeMetadataFilter
+        const embeddings = nodeData.inputs?.embeddings as BaseEmbedding
+        const model = nodeData.inputs?.model
+        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 obj: PineconeParams = {
+            indexName,
+            apiKey: pineconeApiKey
+        }
+
+        if (pineconeNamespace) obj.namespace = pineconeNamespace
+
+        let metadatafilter = {}
+        if (pineconeMetadataFilter) {
+            metadatafilter = typeof pineconeMetadataFilter === 'object' ? pineconeMetadataFilter : JSON.parse(pineconeMetadataFilter)
+            obj.queryFilter = metadatafilter
+        }
+
+        const pcvs = new PineconeVectorStore(obj)
+
+        const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
+        const storageContext = await storageContextFromDefaults({ vectorStore: pcvs })
+
+        const index = await VectorStoreIndex.init({
+            nodes: [],
+            storageContext,
+            serviceContext
+        })
+
+        const output = nodeData.outputs?.output as string
+
+        if (output === 'retriever') {
+            const retriever = index.asRetriever()
+            retriever.similarityTopK = k
+            ;(retriever as any).serviceContext = serviceContext
+            return retriever
+        } else if (output === 'vectorStore') {
+            ;(index as any).k = k
+            if (metadatafilter) {
+                ;(index as any).metadatafilter = metadatafilter
+            }
+            return index
+        }
+        return index
+    }
+}
+
+type PineconeParams = {
+    indexName: string
+    apiKey: string
+    namespace?: string
+    chunkSize?: number
+    queryFilter?: object
+}
+
+class PineconeVectorStore implements VectorStore {
+    storesText: boolean = true
+    db?: Pinecone
+    indexName: string
+    apiKey: string
+    chunkSize: number
+    namespace?: string
+    queryFilter?: object
+
+    constructor(params: PineconeParams) {
+        this.indexName = params?.indexName
+        this.apiKey = params?.apiKey
+        this.namespace = params?.namespace ?? ''
+        this.chunkSize = params?.chunkSize ?? Number.parseInt(process.env.PINECONE_CHUNK_SIZE ?? '100')
+        this.queryFilter = params?.queryFilter ?? {}
+    }
+
+    private async getDb(): Promise<Pinecone> {
+        if (!this.db) {
+            this.db = new Pinecone({
+                apiKey: this.apiKey
+            })
+        }
+        return Promise.resolve(this.db)
+    }
+
+    client() {
+        return this.getDb()
+    }
+
+    async index() {
+        const db: Pinecone = await this.getDb()
+        return db.Index(this.indexName)
+    }
+
+    async clearIndex() {
+        const db: Pinecone = await this.getDb()
+        return await db.index(this.indexName).deleteAll()
+    }
+
+    async add(embeddingResults: BaseNode<Metadata>[]): Promise<string[]> {
+        if (embeddingResults.length == 0) {
+            return Promise.resolve([])
+        }
+
+        const idx: Index = await this.index()
+        const nodes = embeddingResults.map(this.nodeToRecord)
+
+        for (let i = 0; i < nodes.length; i += this.chunkSize) {
+            const chunk = nodes.slice(i, i + this.chunkSize)
+            const result = await this.saveChunk(idx, chunk)
+            if (!result) {
+                return Promise.reject()
+            }
+        }
+        return Promise.resolve([])
+    }
+
+    protected async saveChunk(idx: Index, chunk: any) {
+        try {
+            const namespace = idx.namespace(this.namespace ?? '')
+            await namespace.upsert(chunk)
+            return true
+        } catch (err) {
+            return false
+        }
+    }
+
+    async delete(refDocId: string): Promise<void> {
+        const idx = await this.index()
+        const namespace = idx.namespace(this.namespace ?? '')
+        return namespace.deleteOne(refDocId)
+    }
+
+    async query(query: VectorStoreQuery): Promise<VectorStoreQueryResult> {
+        const queryOptions: any = {
+            vector: query.queryEmbedding,
+            topK: query.similarityTopK,
+            filter: this.queryFilter
+        }
+
+        const idx = await this.index()
+        const namespace = idx.namespace(this.namespace ?? '')
+        const results = await namespace.query(queryOptions)
+
+        const idList = results.matches.map((row) => row.id)
+        const records: FetchResponse<any> = await namespace.fetch(idList)
+        const rows = Object.values(records.records)
+
+        const nodes = rows.map((row) => {
+            return new Document({
+                id_: row.id,
+                text: this.textFromResultRow(row),
+                metadata: this.metaWithoutText(row.metadata),
+                embedding: row.values
+            })
+        })
+
+        const result = {
+            nodes: nodes,
+            similarities: results.matches.map((row) => row.score || 999),
+            ids: results.matches.map((row) => row.id)
+        }
+
+        return Promise.resolve(result)
+    }
+
+    /**
+     * Required by VectorStore interface. Currently ignored.
+     */
+    persist(): Promise<void> {
+        return Promise.resolve()
+    }
+
+    textFromResultRow(row: ScoredPineconeRecord<Metadata>): string {
+        return row.metadata?.text ?? ''
+    }
+
+    metaWithoutText(meta: Metadata): any {
+        return Object.keys(meta)
+            .filter((key) => key != 'text')
+            .reduce((acc: any, key: string) => {
+                acc[key] = meta[key]
+                return acc
+            }, {})
+    }
+
+    nodeToRecord(node: BaseNode<Metadata>) {
+        let id: any = node.id_.length ? node.id_ : null
+        return {
+            id: id,
+            values: node.getEmbedding(),
+            metadata: {
+                ...cleanupMetadata(node.metadata),
+                text: (node as any).text
+            }
+        }
+    }
+}
+
+const cleanupMetadata = (nodeMetadata: ICommonObject) => {
+    // Pinecone doesn't support nested objects, so we flatten them
+    const documentMetadata: any = { ...nodeMetadata }
+    // preserve string arrays which are allowed
+    const stringArrays: Record<string, string[]> = {}
+    for (const key of Object.keys(documentMetadata)) {
+        if (Array.isArray(documentMetadata[key]) && documentMetadata[key].every((el: any) => typeof el === 'string')) {
+            stringArrays[key] = documentMetadata[key]
+            delete documentMetadata[key]
+        }
+    }
+    const metadata: {
+        [key: string]: string | number | boolean | string[] | null
+    } = {
+        ...flattenObject(documentMetadata),
+        ...stringArrays
+    }
+    // Pinecone doesn't support null values, so we remove them
+    for (const key of Object.keys(metadata)) {
+        if (metadata[key] == null) {
+            delete metadata[key]
+        } else if (typeof metadata[key] === 'object' && Object.keys(metadata[key] as unknown as object).length === 0) {
+            delete metadata[key]
+        }
+    }
+    return metadata
+}
+
+module.exports = { nodeClass: PineconeLlamaIndex_VectorStores }

packages/components/nodes/vectorstores/Postgres/Postgres.ts

@@ -24,7 +24,7 @@ class Postgres_VectorStores implements INode {
     constructor() {
         this.label = 'Postgres'
         this.name = 'postgres'
-        this.version = 2.0
+        this.version = 3.0
         this.type = 'Postgres'
         this.icon = 'postgres.svg'
         this.category = 'Vector Stores'
@@ -60,13 +60,6 @@ class Postgres_VectorStores implements INode {
                 name: 'database',
                 type: 'string'
             },
-            {
-                label: 'SSL Connection',
-                name: 'sslConnection',
-                type: 'boolean',
-                default: false,
-                optional: false
-            },
             {
                 label: 'Port',
                 name: 'port',
@@ -124,7 +117,6 @@ class Postgres_VectorStores implements INode {
             const docs = nodeData.inputs?.document as Document[]
             const embeddings = nodeData.inputs?.embeddings as Embeddings
             const additionalConfig = nodeData.inputs?.additionalConfig as string
-            const sslConnection = nodeData.inputs?.sslConnection as boolean
 
             let additionalConfiguration = {}
             if (additionalConfig) {
@@ -142,8 +134,7 @@ class Postgres_VectorStores implements INode {
                 port: nodeData.inputs?.port as number,
                 username: user,
                 password: password,
-                database: nodeData.inputs?.database as string,
-                ssl: sslConnection
+                database: nodeData.inputs?.database as string
             }
 
             const args = {
@@ -198,7 +189,8 @@ class Postgres_VectorStores implements INode {
             type: 'postgres',
             host: nodeData.inputs?.host as string,
             port: nodeData.inputs?.port as number,
-            username: user,
+            username: user, // Required by TypeORMVectorStore
+            user: user, // Required by Pool in similaritySearchVectorWithScore
             password: password,
             database: nodeData.inputs?.database as string
         }
@@ -248,14 +240,7 @@ const similaritySearchVectorWithScore = async (
         ORDER BY "_distance" ASC
         LIMIT $3;`
 
-    const poolOptions = {
-        host: postgresConnectionOptions.host,
-        port: postgresConnectionOptions.port,
-        user: postgresConnectionOptions.username,
-        password: postgresConnectionOptions.password,
-        database: postgresConnectionOptions.database
-    }
-    const pool = new Pool(poolOptions)
+    const pool = new Pool(postgresConnectionOptions)
     const conn = await pool.connect()
 
     const documents = await conn.query(queryString, [embeddingString, _filter, k])

packages/components/nodes/vectorstores/SimpleStore/SimpleStore.ts

@@ -0,0 +1,145 @@
+import path from 'path'
+import { flatten } from 'lodash'
+import { storageContextFromDefaults, serviceContextFromDefaults, VectorStoreIndex, Document } from 'llamaindex'
+import { Document as LCDocument } from 'langchain/document'
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
+import { getUserHome } from '../../../src'
+
+class SimpleStoreUpsert_LlamaIndex_VectorStores implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    tags: string[]
+    inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'SimpleStore'
+        this.name = 'simpleStoreLlamaIndex'
+        this.version = 1.0
+        this.type = 'SimpleVectorStore'
+        this.icon = 'simplevs.svg'
+        this.category = 'Vector Stores'
+        this.description = 'Upsert embedded data to local path and perform similarity search'
+        this.baseClasses = [this.type, 'VectorIndexRetriever']
+        this.tags = ['LlamaIndex']
+        this.inputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                type: 'Document',
+                list: true,
+                optional: true
+            },
+            {
+                label: 'Chat Model',
+                name: 'model',
+                type: 'BaseChatModel_LlamaIndex'
+            },
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'BaseEmbedding_LlamaIndex'
+            },
+            {
+                label: 'Base Path to store',
+                name: 'basePath',
+                description:
+                    'Path to store persist embeddings indexes with persistence. If not specified, default to same path where database is stored',
+                type: 'string',
+                optional: true
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to 4',
+                placeholder: '4',
+                type: 'number',
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'SimpleStore Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'SimpleStore Vector Store Index',
+                name: 'vectorStore',
+                baseClasses: [this.type, 'VectorStoreIndex']
+            }
+        ]
+    }
+
+    //@ts-ignore
+    vectorStoreMethods = {
+        async upsert(nodeData: INodeData): Promise<void> {
+            const basePath = nodeData.inputs?.basePath as string
+            const docs = nodeData.inputs?.document as LCDocument[]
+            const embeddings = nodeData.inputs?.embeddings
+            const model = nodeData.inputs?.model
+
+            let filePath = ''
+            if (!basePath) filePath = path.join(getUserHome(), '.flowise', 'llamaindex')
+            else filePath = basePath
+
+            const flattenDocs = docs && docs.length ? flatten(docs) : []
+            const finalDocs = []
+            for (let i = 0; i < flattenDocs.length; i += 1) {
+                finalDocs.push(new LCDocument(flattenDocs[i]))
+            }
+
+            const llamadocs: Document[] = []
+            for (const doc of finalDocs) {
+                llamadocs.push(new Document({ text: doc.pageContent, metadata: doc.metadata }))
+            }
+
+            const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
+            const storageContext = await storageContextFromDefaults({ persistDir: filePath })
+
+            try {
+                await VectorStoreIndex.fromDocuments(llamadocs, { serviceContext, storageContext })
+            } catch (e) {
+                throw new Error(e)
+            }
+        }
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const basePath = nodeData.inputs?.basePath as string
+        const embeddings = nodeData.inputs?.embeddings
+        const model = nodeData.inputs?.model
+        const topK = nodeData.inputs?.topK as string
+        const k = topK ? parseFloat(topK) : 4
+
+        let filePath = ''
+        if (!basePath) filePath = path.join(getUserHome(), '.flowise', 'llamaindex')
+        else filePath = basePath
+
+        const serviceContext = serviceContextFromDefaults({ llm: model, embedModel: embeddings })
+        const storageContext = await storageContextFromDefaults({ persistDir: filePath })
+
+        const index = await VectorStoreIndex.init({ storageContext, serviceContext })
+
+        const output = nodeData.outputs?.output as string
+
+        if (output === 'retriever') {
+            const retriever = index.asRetriever()
+            retriever.similarityTopK = k
+            ;(retriever as any).serviceContext = serviceContext
+            return retriever
+        } else if (output === 'vectorStore') {
+            ;(index as any).k = k
+            return index
+        }
+        return index
+    }
+}
+
+module.exports = { nodeClass: SimpleStoreUpsert_LlamaIndex_VectorStores }

packages/components/nodes/vectorstores/SimpleStore/simplevs.svg

@@ -0,0 +1,6 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-database" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
+   <path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
+   <path d="M12 6m-8 0a8 3 0 1 0 16 0a8 3 0 1 0 -16 0"></path>
+   <path d="M4 6v6a8 3 0 0 0 16 0v-6"></path>
+   <path d="M4 12v6a8 3 0 0 0 16 0v-6"></path>
+</svg>

packages/components/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise-components",
-    "version": "1.5.3",
+    "version": "1.6.0",
     "description": "Flowiseai Components",
     "main": "dist/src/index",
     "types": "dist/src/index.d.ts",
@@ -62,6 +62,7 @@
         "langfuse-langchain": "2.3.3",
         "langsmith": "0.0.53",
         "linkifyjs": "^4.1.1",
+        "llamaindex": "^0.0.48",
         "llmonitor": "^0.5.5",
         "mammoth": "^1.5.1",
         "moment": "^2.29.3",

packages/components/src/Interface.ts

@@ -98,6 +98,7 @@ export interface INodeProperties {
     version: number
     category: string // TODO: use enum instead of string
     baseClasses: string[]
+    tags?: string[]
     description?: string
     filePath?: string
     badge?: string

packages/components/src/utils.ts

@@ -662,6 +662,28 @@ export const convertSchemaToZod = (schema: string | object): ICommonObject => {
     }
 }
 
+/**
+ * Flatten nested object
+ * @param {ICommonObject} obj
+ * @param {string} parentKey
+ * @returns {ICommonObject}
+ */
+export const flattenObject = (obj: ICommonObject, parentKey?: string) => {
+    let result: any = {}
+
+    Object.keys(obj).forEach((key) => {
+        const value = obj[key]
+        const _key = parentKey ? parentKey + '.' + key : key
+        if (typeof value === 'object') {
+            result = { ...result, ...flattenObject(value, _key) }
+        } else {
+            result[_key] = value
+        }
+    })
+
+    return result
+}
+
 /**
  * Convert BaseMessage to IMessage
  * @param {BaseMessage[]} messages

packages/server/.env.example

@@ -21,6 +21,7 @@ PORT=3000
 # FLOWISE_USERNAME=user
 # FLOWISE_PASSWORD=1234
 # FLOWISE_SECRETKEY_OVERWRITE=myencryptionkey
+# FLOWISE_FILE_SIZE_LIMIT=50mb
 # DEBUG=true
 # LOG_LEVEL=debug (error | warn | info | verbose | debug)
 # TOOL_FUNCTION_BUILTIN_DEP=crypto,fs

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

@@ -1,5 +1,7 @@
 {
     "description": "Use OpenAI Function Agent and Chain to automatically decide which API to call, generating url and body request from conversation",
+    "categories": "Buffer Memory,ChainTool,API Chain,ChatOpenAI,OpenAI Function Agent,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,5 +1,7 @@
 {
     "description": "Given API docs, agent automatically decide which API to call, generating url and body request from conversation",
+    "categories": "Buffer Memory,ChainTool,API Chain,ChatOpenAI,Conversational Agent,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

packages/server/marketplaces/chatflows/Advanced Structured Output Parser.json

@@ -0,0 +1,464 @@
+{
+    "description": "Return response as a JSON structure as specified by a Zod schema",
+    "badge": "NEW",
+    "nodes": [
+        {
+            "width": 300,
+            "height": 508,
+            "id": "llmChain_0",
+            "position": {
+                "x": 1229.1699649849293,
+                "y": 245.55173505632646
+            },
+            "type": "customNode",
+            "data": {
+                "id": "llmChain_0",
+                "label": "LLM Chain",
+                "version": 3,
+                "name": "llmChain",
+                "type": "LLMChain",
+                "baseClasses": ["LLMChain", "BaseChain", "Runnable"],
+                "category": "Chains",
+                "description": "Chain to run queries against LLMs",
+                "inputParams": [
+                    {
+                        "label": "Chain Name",
+                        "name": "chainName",
+                        "type": "string",
+                        "placeholder": "Name Your Chain",
+                        "optional": true,
+                        "id": "llmChain_0-input-chainName-string"
+                    }
+                ],
+                "inputAnchors": [
+                    {
+                        "label": "Language Model",
+                        "name": "model",
+                        "type": "BaseLanguageModel",
+                        "id": "llmChain_0-input-model-BaseLanguageModel"
+                    },
+                    {
+                        "label": "Prompt",
+                        "name": "prompt",
+                        "type": "BasePromptTemplate",
+                        "id": "llmChain_0-input-prompt-BasePromptTemplate"
+                    },
+                    {
+                        "label": "Output Parser",
+                        "name": "outputParser",
+                        "type": "BaseLLMOutputParser",
+                        "optional": true,
+                        "id": "llmChain_0-input-outputParser-BaseLLMOutputParser"
+                    },
+                    {
+                        "label": "Input Moderation",
+                        "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
+                        "name": "inputModeration",
+                        "type": "Moderation",
+                        "optional": true,
+                        "list": true,
+                        "id": "llmChain_0-input-inputModeration-Moderation"
+                    }
+                ],
+                "inputs": {
+                    "model": "{{chatOpenAI_0.data.instance}}",
+                    "prompt": "{{chatPromptTemplate_0.data.instance}}",
+                    "outputParser": "{{advancedStructuredOutputParser_0.data.instance}}",
+                    "chainName": "",
+                    "inputModeration": ""
+                },
+                "outputAnchors": [
+                    {
+                        "name": "output",
+                        "label": "Output",
+                        "type": "options",
+                        "options": [
+                            {
+                                "id": "llmChain_0-output-llmChain-LLMChain|BaseChain|Runnable",
+                                "name": "llmChain",
+                                "label": "LLM Chain",
+                                "type": "LLMChain | BaseChain | Runnable"
+                            },
+                            {
+                                "id": "llmChain_0-output-outputPrediction-string|json",
+                                "name": "outputPrediction",
+                                "label": "Output Prediction",
+                                "type": "string | json"
+                            }
+                        ],
+                        "default": "llmChain"
+                    }
+                ],
+                "outputs": {
+                    "output": "llmChain"
+                },
+                "selected": false
+            },
+            "positionAbsolute": {
+                "x": 1229.1699649849293,
+                "y": 245.55173505632646
+            },
+            "selected": false
+        },
+        {
+            "width": 300,
+            "height": 690,
+            "id": "chatPromptTemplate_0",
+            "position": {
+                "x": 493.26582927222483,
+                "y": -156.20470841335592
+            },
+            "type": "customNode",
+            "data": {
+                "id": "chatPromptTemplate_0",
+                "label": "Chat Prompt Template",
+                "version": 1,
+                "name": "chatPromptTemplate",
+                "type": "ChatPromptTemplate",
+                "baseClasses": ["ChatPromptTemplate", "BaseChatPromptTemplate", "BasePromptTemplate", "Runnable"],
+                "category": "Prompts",
+                "description": "Schema to represent a chat prompt",
+                "inputParams": [
+                    {
+                        "label": "System Message",
+                        "name": "systemMessagePrompt",
+                        "type": "string",
+                        "rows": 4,
+                        "placeholder": "You are a helpful assistant that translates {input_language} to {output_language}.",
+                        "id": "chatPromptTemplate_0-input-systemMessagePrompt-string"
+                    },
+                    {
+                        "label": "Human Message",
+                        "name": "humanMessagePrompt",
+                        "type": "string",
+                        "rows": 4,
+                        "placeholder": "{text}",
+                        "id": "chatPromptTemplate_0-input-humanMessagePrompt-string"
+                    },
+                    {
+                        "label": "Format Prompt Values",
+                        "name": "promptValues",
+                        "type": "json",
+                        "optional": true,
+                        "acceptVariable": true,
+                        "list": true,
+                        "id": "chatPromptTemplate_0-input-promptValues-json"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "systemMessagePrompt": "This AI is designed to only output information in JSON format without exception. This AI can only output JSON and will never output any other text.\n\nWhen asked to correct itself, this AI will only output the corrected JSON and never any other text.",
+                    "humanMessagePrompt": "{text}",
+                    "promptValues": ""
+                },
+                "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": 493.26582927222483,
+                "y": -156.20470841335592
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 576,
+            "id": "chatOpenAI_0",
+            "position": {
+                "x": 860.555928011636,
+                "y": -355.71028569475095
+            },
+            "type": "customNode",
+            "data": {
+                "id": "chatOpenAI_0",
+                "label": "ChatOpenAI",
+                "version": 3,
+                "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-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "label": "gpt-4-1106-preview",
+                                "name": "gpt-4-1106-preview"
+                            },
+                            {
+                                "label": "gpt-4-vision-preview",
+                                "name": "gpt-4-vision-preview"
+                            },
+                            {
+                                "label": "gpt-4-0613",
+                                "name": "gpt-4-0613"
+                            },
+                            {
+                                "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-0125",
+                                "name": "gpt-3.5-turbo-0125"
+                            },
+                            {
+                                "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": "",
+                    "temperature": "0",
+                    "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": 860.555928011636,
+                "y": -355.71028569475095
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 454,
+            "id": "advancedStructuredOutputParser_0",
+            "position": {
+                "x": 489.3637511211284,
+                "y": 580.0628053662244
+            },
+            "type": "customNode",
+            "data": {
+                "id": "advancedStructuredOutputParser_0",
+                "label": "Advanced Structured Output Parser",
+                "version": 1,
+                "name": "advancedStructuredOutputParser",
+                "type": "AdvancedStructuredOutputParser",
+                "baseClasses": ["AdvancedStructuredOutputParser", "BaseLLMOutputParser", "Runnable"],
+                "category": "Output Parsers",
+                "description": "Parse the output of an LLM call into a given structure by providing a Zod schema.",
+                "inputParams": [
+                    {
+                        "label": "Autofix",
+                        "name": "autofixParser",
+                        "type": "boolean",
+                        "optional": true,
+                        "description": "In the event that the first call fails, will make another call to the model to fix any errors.",
+                        "id": "advancedStructuredOutputParser_0-input-autofixParser-boolean"
+                    },
+                    {
+                        "label": "Example JSON",
+                        "name": "exampleJson",
+                        "type": "string",
+                        "description": "Zod schema for the output of the model",
+                        "rows": 10,
+                        "default": "z.object({\n    title: z.string(), // Title of the movie as a string\n    yearOfRelease: z.number().int(), // Release year as an integer number,\n    genres: z.enum([\n        \"Action\", \"Comedy\", \"Drama\", \"Fantasy\", \"Horror\",\n        \"Mystery\", \"Romance\", \"Science Fiction\", \"Thriller\", \"Documentary\"\n    ]).array().max(2), // Array of genres, max of 2 from the defined enum\n    shortDescription: z.string().max(500) // Short description, max 500 characters\n})",
+                        "id": "advancedStructuredOutputParser_0-input-exampleJson-string"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "autofixParser": "",
+                    "exampleJson": "z.object({\n    title: z.string(), // Title of the movie as a string\n    yearOfRelease: z.number().int(), // Release year as an integer number,\n    genres: z.enum([\n        \"Action\", \"Comedy\", \"Drama\", \"Fantasy\", \"Horror\",\n        \"Mystery\", \"Romance\", \"Science Fiction\", \"Thriller\", \"Documentary\"\n    ]).array().max(2), // Array of genres, max of 2 from the defined enum\n    shortDescription: z.string().max(500) // Short description, max 500 characters\n})"
+                },
+                "outputAnchors": [
+                    {
+                        "id": "advancedStructuredOutputParser_0-output-advancedStructuredOutputParser-AdvancedStructuredOutputParser|BaseLLMOutputParser|Runnable",
+                        "name": "advancedStructuredOutputParser",
+                        "label": "AdvancedStructuredOutputParser",
+                        "type": "AdvancedStructuredOutputParser | BaseLLMOutputParser | Runnable"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "dragging": false,
+            "positionAbsolute": {
+                "x": 489.3637511211284,
+                "y": 580.0628053662244
+            }
+        }
+    ],
+    "edges": [
+        {
+            "source": "chatPromptTemplate_0",
+            "sourceHandle": "chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable",
+            "target": "llmChain_0",
+            "targetHandle": "llmChain_0-input-prompt-BasePromptTemplate",
+            "type": "buttonedge",
+            "id": "chatPromptTemplate_0-chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable-llmChain_0-llmChain_0-input-prompt-BasePromptTemplate",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "chatOpenAI_0",
+            "sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
+            "target": "llmChain_0",
+            "targetHandle": "llmChain_0-input-model-BaseLanguageModel",
+            "type": "buttonedge",
+            "id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-llmChain_0-llmChain_0-input-model-BaseLanguageModel"
+        },
+        {
+            "source": "advancedStructuredOutputParser_0",
+            "sourceHandle": "advancedStructuredOutputParser_0-output-advancedStructuredOutputParser-AdvancedStructuredOutputParser|BaseLLMOutputParser|Runnable",
+            "target": "llmChain_0",
+            "targetHandle": "llmChain_0-input-outputParser-BaseLLMOutputParser",
+            "type": "buttonedge",
+            "id": "advancedStructuredOutputParser_0-advancedStructuredOutputParser_0-output-advancedStructuredOutputParser-AdvancedStructuredOutputParser|BaseLLMOutputParser|Runnable-llmChain_0-llmChain_0-input-outputParser-BaseLLMOutputParser"
+        }
+    ]
+}

packages/server/marketplaces/chatflows/Antonym.json

@@ -1,5 +1,7 @@
 {
     "description": "Output antonym of given user input using few-shot prompt template built with examples",
+    "categories": "Few Shot Prompt,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

packages/server/marketplaces/chatflows/AutoGPT.json

@@ -1,5 +1,7 @@
 {
     "description": "Use AutoGPT - Autonomous agent with chain of thoughts for self-guided task completion",
+    "categories": "AutoGPT,SERP Tool,File Read/Write,ChatOpenAI,Pinecone,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

packages/server/marketplaces/chatflows/BabyAGI.json

@@ -1,5 +1,7 @@
 {
     "description": "Use BabyAGI to create tasks and reprioritize for a given objective",
+    "categories": "BabyAGI,ChatOpenAI,Pinecone,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,5 +1,7 @@
 {
     "description": "Analyse and summarize CSV data",
+    "categories": "CSV Agent,ChatOpenAI,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,5 +1,7 @@
 {
     "description": "Engage with data sources such as YouTube Transcripts, Google, and more through intelligent Q&A interactions",
+    "categories": "Memory Vector Store,SearchAPI,ChatOpenAI,Conversational Retrieval QA Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

packages/server/marketplaces/chatflows/ChatGPTPlugin.json

@@ -1,5 +1,7 @@
 {
     "description": "Use ChatGPT Plugins within LangChain abstractions with GET and POST Tools",
+    "categories": "ChatGPT Plugin,HTTP GET/POST,ChatOpenAI,MRKL Agent,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,5 +1,7 @@
 {
     "description": "Use Anthropic Claude with 200k context window to ingest whole document for QnA",
+    "categories": "Buffer Memory,Prompt Template,Conversation Chain,ChatAnthropic,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,
@@ -439,10 +441,10 @@
                         "type": "options",
                         "options": [
                             {
-                                "id": "plainText_0-output-document-Document",
+                                "id": "plainText_0-output-document-Document|json",
                                 "name": "document",
                                 "label": "Document",
-                                "type": "Document"
+                                "type": "Document | json"
                             },
                             {
                                 "id": "plainText_0-output-text-string|json",

packages/server/marketplaces/chatflows/Context Chat Engine.json

@@ -0,0 +1,919 @@
+{
+    "description": "Answer question based on retrieved documents (context) with built-in memory to remember conversation using LlamaIndex",
+    "categories": "Text File,Prompt Template,ChatOpenAI,Conversation Chain,Pinecone,LlamaIndex,Redis",
+    "framework": "LlamaIndex",
+    "badge": "NEW",
+    "nodes": [
+        {
+            "width": 300,
+            "height": 438,
+            "id": "textFile_0",
+            "position": {
+                "x": 221.215421786192,
+                "y": 94.91489477412404
+            },
+            "type": "customNode",
+            "data": {
+                "id": "textFile_0",
+                "label": "Text File",
+                "version": 3,
+                "name": "textFile",
+                "type": "Document",
+                "baseClasses": ["Document"],
+                "category": "Document Loaders",
+                "description": "Load data from text files",
+                "inputParams": [
+                    {
+                        "label": "Txt File",
+                        "name": "txtFile",
+                        "type": "file",
+                        "fileType": ".txt, .html, .aspx, .asp, .cpp, .c, .cs, .css, .go, .h, .java, .js, .less, .ts, .php, .proto, .python, .py, .rst, .ruby, .rb, .rs, .scala, .sc, .scss, .sol, .sql, .swift, .markdown, .md, .tex, .ltx, .vb, .xml",
+                        "id": "textFile_0-input-txtFile-file"
+                    },
+                    {
+                        "label": "Metadata",
+                        "name": "metadata",
+                        "type": "json",
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "textFile_0-input-metadata-json"
+                    }
+                ],
+                "inputAnchors": [
+                    {
+                        "label": "Text Splitter",
+                        "name": "textSplitter",
+                        "type": "TextSplitter",
+                        "optional": true,
+                        "id": "textFile_0-input-textSplitter-TextSplitter"
+                    }
+                ],
+                "inputs": {
+                    "textSplitter": "{{recursiveCharacterTextSplitter_0.data.instance}}",
+                    "metadata": ""
+                },
+                "outputAnchors": [
+                    {
+                        "name": "output",
+                        "label": "Output",
+                        "type": "options",
+                        "options": [
+                            {
+                                "id": "textFile_0-output-document-Document|json",
+                                "name": "document",
+                                "label": "Document",
+                                "type": "Document | json"
+                            },
+                            {
+                                "id": "textFile_0-output-text-string|json",
+                                "name": "text",
+                                "label": "Text",
+                                "type": "string | json"
+                            }
+                        ],
+                        "default": "document"
+                    }
+                ],
+                "outputs": {
+                    "output": "document"
+                },
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 221.215421786192,
+                "y": 94.91489477412404
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 429,
+            "id": "recursiveCharacterTextSplitter_0",
+            "position": {
+                "x": -203.4868320229876,
+                "y": 101.32475976329766
+            },
+            "type": "customNode",
+            "data": {
+                "id": "recursiveCharacterTextSplitter_0",
+                "label": "Recursive Character Text Splitter",
+                "version": 2,
+                "name": "recursiveCharacterTextSplitter",
+                "type": "RecursiveCharacterTextSplitter",
+                "baseClasses": ["RecursiveCharacterTextSplitter", "TextSplitter", "BaseDocumentTransformer", "Runnable"],
+                "category": "Text Splitters",
+                "description": "Split documents recursively by different characters - starting with \"\\n\\n\", then \"\\n\", then \" \"",
+                "inputParams": [
+                    {
+                        "label": "Chunk Size",
+                        "name": "chunkSize",
+                        "type": "number",
+                        "default": 1000,
+                        "optional": true,
+                        "id": "recursiveCharacterTextSplitter_0-input-chunkSize-number"
+                    },
+                    {
+                        "label": "Chunk Overlap",
+                        "name": "chunkOverlap",
+                        "type": "number",
+                        "optional": true,
+                        "id": "recursiveCharacterTextSplitter_0-input-chunkOverlap-number"
+                    },
+                    {
+                        "label": "Custom Separators",
+                        "name": "separators",
+                        "type": "string",
+                        "rows": 4,
+                        "description": "Array of custom separators to determine when to split the text, will override the default separators",
+                        "placeholder": "[\"|\", \"##\", \">\", \"-\"]",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "recursiveCharacterTextSplitter_0-input-separators-string"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "chunkSize": 1000,
+                    "chunkOverlap": "",
+                    "separators": ""
+                },
+                "outputAnchors": [
+                    {
+                        "id": "recursiveCharacterTextSplitter_0-output-recursiveCharacterTextSplitter-RecursiveCharacterTextSplitter|TextSplitter|BaseDocumentTransformer|Runnable",
+                        "name": "recursiveCharacterTextSplitter",
+                        "label": "RecursiveCharacterTextSplitter",
+                        "type": "RecursiveCharacterTextSplitter | TextSplitter | BaseDocumentTransformer | Runnable"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": -203.4868320229876,
+                "y": 101.32475976329766
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 334,
+            "id": "openAIEmbedding_LlamaIndex_0",
+            "position": {
+                "x": 176.27434578083106,
+                "y": 953.3664298122493
+            },
+            "type": "customNode",
+            "data": {
+                "id": "openAIEmbedding_LlamaIndex_0",
+                "label": "OpenAI Embedding",
+                "version": 1,
+                "name": "openAIEmbedding_LlamaIndex",
+                "type": "OpenAIEmbedding",
+                "baseClasses": ["OpenAIEmbedding", "BaseEmbedding_LlamaIndex", "BaseEmbedding"],
+                "tags": ["LlamaIndex"],
+                "category": "Embeddings",
+                "description": "OpenAI Embedding specific for LlamaIndex",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["openAIApi"],
+                        "id": "openAIEmbedding_LlamaIndex_0-input-credential-credential"
+                    },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbedding_LlamaIndex_0-input-modelName-options"
+                    },
+                    {
+                        "label": "Timeout",
+                        "name": "timeout",
+                        "type": "number",
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "openAIEmbedding_LlamaIndex_0-input-timeout-number"
+                    },
+                    {
+                        "label": "BasePath",
+                        "name": "basepath",
+                        "type": "string",
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "openAIEmbedding_LlamaIndex_0-input-basepath-string"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "timeout": "",
+                    "basepath": ""
+                },
+                "outputAnchors": [
+                    {
+                        "id": "openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding",
+                        "name": "openAIEmbedding_LlamaIndex",
+                        "label": "OpenAIEmbedding",
+                        "type": "OpenAIEmbedding | BaseEmbedding_LlamaIndex | BaseEmbedding"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 176.27434578083106,
+                "y": 953.3664298122493
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 585,
+            "id": "pineconeLlamaIndex_0",
+            "position": {
+                "x": 609.3087433345761,
+                "y": 488.2141798951578
+            },
+            "type": "customNode",
+            "data": {
+                "id": "pineconeLlamaIndex_0",
+                "label": "Pinecone",
+                "version": 1,
+                "name": "pineconeLlamaIndex",
+                "type": "Pinecone",
+                "baseClasses": ["Pinecone", "VectorIndexRetriever"],
+                "tags": ["LlamaIndex"],
+                "category": "Vector Stores",
+                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["pineconeApi"],
+                        "id": "pineconeLlamaIndex_0-input-credential-credential"
+                    },
+                    {
+                        "label": "Pinecone Index",
+                        "name": "pineconeIndex",
+                        "type": "string",
+                        "id": "pineconeLlamaIndex_0-input-pineconeIndex-string"
+                    },
+                    {
+                        "label": "Pinecone Namespace",
+                        "name": "pineconeNamespace",
+                        "type": "string",
+                        "placeholder": "my-first-namespace",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pineconeLlamaIndex_0-input-pineconeNamespace-string"
+                    },
+                    {
+                        "label": "Pinecone Metadata Filter",
+                        "name": "pineconeMetadataFilter",
+                        "type": "json",
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "pineconeLlamaIndex_0-input-pineconeMetadataFilter-json"
+                    },
+                    {
+                        "label": "Top K",
+                        "name": "topK",
+                        "description": "Number of top results to fetch. Default to 4",
+                        "placeholder": "4",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pineconeLlamaIndex_0-input-topK-number"
+                    }
+                ],
+                "inputAnchors": [
+                    {
+                        "label": "Document",
+                        "name": "document",
+                        "type": "Document",
+                        "list": true,
+                        "optional": true,
+                        "id": "pineconeLlamaIndex_0-input-document-Document"
+                    },
+                    {
+                        "label": "Chat Model",
+                        "name": "model",
+                        "type": "BaseChatModel_LlamaIndex",
+                        "id": "pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex"
+                    },
+                    {
+                        "label": "Embeddings",
+                        "name": "embeddings",
+                        "type": "BaseEmbedding_LlamaIndex",
+                        "id": "pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex"
+                    }
+                ],
+                "inputs": {
+                    "document": ["{{textFile_0.data.instance}}"],
+                    "model": "{{chatOpenAI_LlamaIndex_1.data.instance}}",
+                    "embeddings": "{{openAIEmbedding_LlamaIndex_0.data.instance}}",
+                    "pineconeIndex": "",
+                    "pineconeNamespace": "",
+                    "pineconeMetadataFilter": "",
+                    "topK": ""
+                },
+                "outputAnchors": [
+                    {
+                        "name": "output",
+                        "label": "Output",
+                        "type": "options",
+                        "options": [
+                            {
+                                "id": "pineconeLlamaIndex_0-output-retriever-Pinecone|VectorIndexRetriever",
+                                "name": "retriever",
+                                "label": "Pinecone Retriever",
+                                "type": "Pinecone | VectorIndexRetriever"
+                            },
+                            {
+                                "id": "pineconeLlamaIndex_0-output-retriever-Pinecone|VectorStoreIndex",
+                                "name": "vectorStore",
+                                "label": "Pinecone Vector Store Index",
+                                "type": "Pinecone | VectorStoreIndex"
+                            }
+                        ],
+                        "default": "retriever"
+                    }
+                ],
+                "outputs": {
+                    "output": "retriever"
+                },
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 609.3087433345761,
+                "y": 488.2141798951578
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 529,
+            "id": "chatOpenAI_LlamaIndex_1",
+            "position": {
+                "x": -195.15244974578656,
+                "y": 584.9467028201428
+            },
+            "type": "customNode",
+            "data": {
+                "id": "chatOpenAI_LlamaIndex_1",
+                "label": "ChatOpenAI",
+                "version": 1,
+                "name": "chatOpenAI_LlamaIndex",
+                "type": "ChatOpenAI",
+                "baseClasses": ["ChatOpenAI", "BaseChatModel_LlamaIndex"],
+                "tags": ["LlamaIndex"],
+                "category": "Chat Models",
+                "description": "Wrapper around OpenAI Chat LLM specific for LlamaIndex",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["openAIApi"],
+                        "id": "chatOpenAI_LlamaIndex_1-input-credential-credential"
+                    },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "gpt-4",
+                                "name": "gpt-4"
+                            },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "label": "gpt-4-1106-preview",
+                                "name": "gpt-4-1106-preview"
+                            },
+                            {
+                                "label": "gpt-4-vision-preview",
+                                "name": "gpt-4-vision-preview"
+                            },
+                            {
+                                "label": "gpt-4-0613",
+                                "name": "gpt-4-0613"
+                            },
+                            {
+                                "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_LlamaIndex_1-input-modelName-options"
+                    },
+                    {
+                        "label": "Temperature",
+                        "name": "temperature",
+                        "type": "number",
+                        "step": 0.1,
+                        "default": 0.9,
+                        "optional": true,
+                        "id": "chatOpenAI_LlamaIndex_1-input-temperature-number"
+                    },
+                    {
+                        "label": "Max Tokens",
+                        "name": "maxTokens",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_LlamaIndex_1-input-maxTokens-number"
+                    },
+                    {
+                        "label": "Top Probability",
+                        "name": "topP",
+                        "type": "number",
+                        "step": 0.1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_LlamaIndex_1-input-topP-number"
+                    },
+                    {
+                        "label": "Timeout",
+                        "name": "timeout",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_LlamaIndex_1-input-timeout-number"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "modelName": "gpt-3.5-turbo-16k",
+                    "temperature": 0.9,
+                    "maxTokens": "",
+                    "topP": "",
+                    "timeout": ""
+                },
+                "outputAnchors": [
+                    {
+                        "id": "chatOpenAI_LlamaIndex_1-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex",
+                        "name": "chatOpenAI_LlamaIndex",
+                        "label": "ChatOpenAI",
+                        "type": "ChatOpenAI | BaseChatModel_LlamaIndex"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": -195.15244974578656,
+                "y": 584.9467028201428
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 513,
+            "id": "contextChatEngine_0",
+            "position": {
+                "x": 1550.2553933740128,
+                "y": 270.7914631777829
+            },
+            "type": "customNode",
+            "data": {
+                "id": "contextChatEngine_0",
+                "label": "Context Chat Engine",
+                "version": 1,
+                "name": "contextChatEngine",
+                "type": "ContextChatEngine",
+                "baseClasses": ["ContextChatEngine"],
+                "tags": ["LlamaIndex"],
+                "category": "Engine",
+                "description": "Answer question based on retrieved documents (context) with built-in memory to remember conversation",
+                "inputParams": [
+                    {
+                        "label": "Return Source Documents",
+                        "name": "returnSourceDocuments",
+                        "type": "boolean",
+                        "optional": true,
+                        "id": "contextChatEngine_0-input-returnSourceDocuments-boolean"
+                    },
+                    {
+                        "label": "System Message",
+                        "name": "systemMessagePrompt",
+                        "type": "string",
+                        "rows": 4,
+                        "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.",
+                        "id": "contextChatEngine_0-input-systemMessagePrompt-string"
+                    }
+                ],
+                "inputAnchors": [
+                    {
+                        "label": "Chat Model",
+                        "name": "model",
+                        "type": "BaseChatModel_LlamaIndex",
+                        "id": "contextChatEngine_0-input-model-BaseChatModel_LlamaIndex"
+                    },
+                    {
+                        "label": "Vector Store Retriever",
+                        "name": "vectorStoreRetriever",
+                        "type": "VectorIndexRetriever",
+                        "id": "contextChatEngine_0-input-vectorStoreRetriever-VectorIndexRetriever"
+                    },
+                    {
+                        "label": "Memory",
+                        "name": "memory",
+                        "type": "BaseChatMemory",
+                        "id": "contextChatEngine_0-input-memory-BaseChatMemory"
+                    }
+                ],
+                "inputs": {
+                    "model": "{{chatOpenAI_LlamaIndex_2.data.instance}}",
+                    "vectorStoreRetriever": "{{pineconeLlamaIndex_0.data.instance}}",
+                    "memory": "{{RedisBackedChatMemory_0.data.instance}}",
+                    "systemMessagePrompt": "",
+                    "returnSourceDocuments": true
+                },
+                "outputAnchors": [
+                    {
+                        "id": "contextChatEngine_0-output-contextChatEngine-ContextChatEngine",
+                        "name": "contextChatEngine",
+                        "label": "ContextChatEngine",
+                        "type": "ContextChatEngine"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 1550.2553933740128,
+                "y": 270.7914631777829
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 329,
+            "id": "RedisBackedChatMemory_0",
+            "position": {
+                "x": 1081.252815805786,
+                "y": 990.1701092562037
+            },
+            "type": "customNode",
+            "data": {
+                "id": "RedisBackedChatMemory_0",
+                "label": "Redis-Backed Chat Memory",
+                "version": 2,
+                "name": "RedisBackedChatMemory",
+                "type": "RedisBackedChatMemory",
+                "baseClasses": ["RedisBackedChatMemory", "BaseChatMemory", "BaseMemory"],
+                "category": "Memory",
+                "description": "Summarizes the conversation and stores the memory in Redis server",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "optional": true,
+                        "credentialNames": ["redisCacheApi", "redisCacheUrlApi"],
+                        "id": "RedisBackedChatMemory_0-input-credential-credential"
+                    },
+                    {
+                        "label": "Session Id",
+                        "name": "sessionId",
+                        "type": "string",
+                        "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,
+                        "id": "RedisBackedChatMemory_0-input-sessionId-string"
+                    },
+                    {
+                        "label": "Session Timeouts",
+                        "name": "sessionTTL",
+                        "type": "number",
+                        "description": "Omit this parameter to make sessions never expire",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "RedisBackedChatMemory_0-input-sessionTTL-number"
+                    },
+                    {
+                        "label": "Memory Key",
+                        "name": "memoryKey",
+                        "type": "string",
+                        "default": "chat_history",
+                        "additionalParams": true,
+                        "id": "RedisBackedChatMemory_0-input-memoryKey-string"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "sessionId": "",
+                    "sessionTTL": "",
+                    "memoryKey": "chat_history"
+                },
+                "outputAnchors": [
+                    {
+                        "id": "RedisBackedChatMemory_0-output-RedisBackedChatMemory-RedisBackedChatMemory|BaseChatMemory|BaseMemory",
+                        "name": "RedisBackedChatMemory",
+                        "label": "RedisBackedChatMemory",
+                        "type": "RedisBackedChatMemory | BaseChatMemory | BaseMemory"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "dragging": false,
+            "positionAbsolute": {
+                "x": 1081.252815805786,
+                "y": 990.1701092562037
+            }
+        },
+        {
+            "width": 300,
+            "height": 529,
+            "id": "chatOpenAI_LlamaIndex_2",
+            "position": {
+                "x": 1015.1605888108386,
+                "y": -38.31143117572401
+            },
+            "type": "customNode",
+            "data": {
+                "id": "chatOpenAI_LlamaIndex_2",
+                "label": "ChatOpenAI",
+                "version": 1,
+                "name": "chatOpenAI_LlamaIndex",
+                "type": "ChatOpenAI",
+                "baseClasses": ["ChatOpenAI", "BaseChatModel_LlamaIndex"],
+                "tags": ["LlamaIndex"],
+                "category": "Chat Models",
+                "description": "Wrapper around OpenAI Chat LLM specific for LlamaIndex",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["openAIApi"],
+                        "id": "chatOpenAI_LlamaIndex_2-input-credential-credential"
+                    },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "gpt-4",
+                                "name": "gpt-4"
+                            },
+                            {
+                                "label": "gpt-4-turbo-preview",
+                                "name": "gpt-4-turbo-preview"
+                            },
+                            {
+                                "label": "gpt-4-0125-preview",
+                                "name": "gpt-4-0125-preview"
+                            },
+                            {
+                                "label": "gpt-4-1106-preview",
+                                "name": "gpt-4-1106-preview"
+                            },
+                            {
+                                "label": "gpt-4-vision-preview",
+                                "name": "gpt-4-vision-preview"
+                            },
+                            {
+                                "label": "gpt-4-0613",
+                                "name": "gpt-4-0613"
+                            },
+                            {
+                                "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_LlamaIndex_2-input-modelName-options"
+                    },
+                    {
+                        "label": "Temperature",
+                        "name": "temperature",
+                        "type": "number",
+                        "step": 0.1,
+                        "default": 0.9,
+                        "optional": true,
+                        "id": "chatOpenAI_LlamaIndex_2-input-temperature-number"
+                    },
+                    {
+                        "label": "Max Tokens",
+                        "name": "maxTokens",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_LlamaIndex_2-input-maxTokens-number"
+                    },
+                    {
+                        "label": "Top Probability",
+                        "name": "topP",
+                        "type": "number",
+                        "step": 0.1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_LlamaIndex_2-input-topP-number"
+                    },
+                    {
+                        "label": "Timeout",
+                        "name": "timeout",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatOpenAI_LlamaIndex_2-input-timeout-number"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "modelName": "gpt-3.5-turbo",
+                    "temperature": 0.9,
+                    "maxTokens": "",
+                    "topP": "",
+                    "timeout": ""
+                },
+                "outputAnchors": [
+                    {
+                        "id": "chatOpenAI_LlamaIndex_2-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex",
+                        "name": "chatOpenAI_LlamaIndex",
+                        "label": "ChatOpenAI",
+                        "type": "ChatOpenAI | BaseChatModel_LlamaIndex"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 1015.1605888108386,
+                "y": -38.31143117572401
+            },
+            "dragging": false
+        }
+    ],
+    "edges": [
+        {
+            "source": "recursiveCharacterTextSplitter_0",
+            "sourceHandle": "recursiveCharacterTextSplitter_0-output-recursiveCharacterTextSplitter-RecursiveCharacterTextSplitter|TextSplitter|BaseDocumentTransformer|Runnable",
+            "target": "textFile_0",
+            "targetHandle": "textFile_0-input-textSplitter-TextSplitter",
+            "type": "buttonedge",
+            "id": "recursiveCharacterTextSplitter_0-recursiveCharacterTextSplitter_0-output-recursiveCharacterTextSplitter-RecursiveCharacterTextSplitter|TextSplitter|BaseDocumentTransformer|Runnable-textFile_0-textFile_0-input-textSplitter-TextSplitter",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "textFile_0",
+            "sourceHandle": "textFile_0-output-document-Document|json",
+            "target": "pineconeLlamaIndex_0",
+            "targetHandle": "pineconeLlamaIndex_0-input-document-Document",
+            "type": "buttonedge",
+            "id": "textFile_0-textFile_0-output-document-Document|json-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-document-Document",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "chatOpenAI_LlamaIndex_1",
+            "sourceHandle": "chatOpenAI_LlamaIndex_1-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex",
+            "target": "pineconeLlamaIndex_0",
+            "targetHandle": "pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex",
+            "type": "buttonedge",
+            "id": "chatOpenAI_LlamaIndex_1-chatOpenAI_LlamaIndex_1-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "openAIEmbedding_LlamaIndex_0",
+            "sourceHandle": "openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding",
+            "target": "pineconeLlamaIndex_0",
+            "targetHandle": "pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex",
+            "type": "buttonedge",
+            "id": "openAIEmbedding_LlamaIndex_0-openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "pineconeLlamaIndex_0",
+            "sourceHandle": "pineconeLlamaIndex_0-output-pineconeLlamaIndex-Pinecone|VectorIndexRetriever",
+            "target": "contextChatEngine_0",
+            "targetHandle": "contextChatEngine_0-input-vectorStoreRetriever-VectorIndexRetriever",
+            "type": "buttonedge",
+            "id": "pineconeLlamaIndex_0-pineconeLlamaIndex_0-output-pineconeLlamaIndex-Pinecone|VectorIndexRetriever-contextChatEngine_0-contextChatEngine_0-input-vectorStoreRetriever-VectorIndexRetriever",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "RedisBackedChatMemory_0",
+            "sourceHandle": "RedisBackedChatMemory_0-output-RedisBackedChatMemory-RedisBackedChatMemory|BaseChatMemory|BaseMemory",
+            "target": "contextChatEngine_0",
+            "targetHandle": "contextChatEngine_0-input-memory-BaseChatMemory",
+            "type": "buttonedge",
+            "id": "RedisBackedChatMemory_0-RedisBackedChatMemory_0-output-RedisBackedChatMemory-RedisBackedChatMemory|BaseChatMemory|BaseMemory-contextChatEngine_0-contextChatEngine_0-input-memory-BaseChatMemory",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "chatOpenAI_LlamaIndex_2",
+            "sourceHandle": "chatOpenAI_LlamaIndex_2-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex",
+            "target": "contextChatEngine_0",
+            "targetHandle": "contextChatEngine_0-input-model-BaseChatModel_LlamaIndex",
+            "type": "buttonedge",
+            "id": "chatOpenAI_LlamaIndex_2-chatOpenAI_LlamaIndex_2-output-chatOpenAI_LlamaIndex-ChatOpenAI|BaseChatModel_LlamaIndex-contextChatEngine_0-contextChatEngine_0-input-model-BaseChatModel_LlamaIndex",
+            "data": {
+                "label": ""
+            }
+        }
+    ]
+}

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

@@ -1,5 +1,7 @@
 {
     "description": "A conversational agent for a chat model which utilize chat specific prompts",
+    "categories": "Calculator Tool,Buffer Memory,SerpAPI,ChatOpenAI,Conversational Agent,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,6 +1,8 @@
 {
     "description": "Agent optimized for vector retrieval during conversation and answering questions based on previous dialogue.",
+    "categories": "Retriever Tool,Buffer Memory,ChatOpenAI,Conversational Retrieval Agent, Pinecone,Langchain",
     "badge": "POPULAR",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,6 +1,8 @@
 {
     "description": "Text file QnA using conversational retrieval QA chain",
+    "categories": "TextFile,ChatOpenAI,Conversational Retrieval QA Chain,Pinecone,Langchain",
     "badge": "POPULAR",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,
@@ -233,10 +235,10 @@
                         "type": "options",
                         "options": [
                             {
-                                "id": "textFile_0-output-document-Document",
+                                "id": "textFile_0-output-document-Document|json",
                                 "name": "document",
                                 "label": "Document",
-                                "type": "Document"
+                                "type": "Document | json"
                             },
                             {
                                 "id": "textFile_0-output-text-string|json",
@@ -730,11 +732,11 @@
         },
         {
             "source": "textFile_0",
-            "sourceHandle": "textFile_0-output-document-Document",
+            "sourceHandle": "textFile_0-output-document-Document|json",
             "target": "pinecone_0",
             "targetHandle": "pinecone_0-input-document-Document",
             "type": "buttonedge",
-            "id": "textFile_0-textFile_0-output-document-Document-pinecone_0-pinecone_0-input-document-Document",
+            "id": "textFile_0-textFile_0-output-document-Document|json-pinecone_0-pinecone_0-input-document-Document",
             "data": {
                 "label": ""
             }

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

@@ -1,6 +1,8 @@
 {
     "description": "Flowise Docs Github QnA using conversational retrieval QA chain",
+    "categories": "Memory Vector Store,Github Loader,ChatOpenAI,Conversational Retrieval QA Chain,Langchain",
     "badge": "POPULAR",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,5 +1,7 @@
 {
     "description": "Simple LLM Chain using HuggingFace Inference API on falcon-7b-instruct model",
+    "categories": "HuggingFace,LLM Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

packages/server/marketplaces/chatflows/IfElse.json

@@ -1,5 +1,7 @@
 {
     "description": "Split flows based on if else condition",
+    "categories": "IfElse Function,ChatOpenAI,OpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
     "badge": "new",
     "nodes": [
         {

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

@@ -1,6 +1,8 @@
 {
     "description": "Generate image using Replicate Stability text-to-image generative AI model",
     "badge": "NEW",
+    "categories": "Replicate,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,6 +1,8 @@
 {
     "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
     "badge": "NEW",
+    "categories": "Moderation,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,6 +1,8 @@
 {
     "description": "Return response as a list (array) instead of a string/text",
     "badge": "NEW",
+    "categories": "CSV Output Parser,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,6 +1,8 @@
 {
     "description": "QnA chain using Ollama local LLM, LocalAI embedding model, and Faiss local vector store",
     "badge": "POPULAR",
+    "categories": "Text File,ChatOllama,Conversational Retrieval QA Chain,Faiss,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,
@@ -224,10 +226,10 @@
                         "type": "options",
                         "options": [
                             {
-                                "id": "textFile_0-output-document-Document",
+                                "id": "textFile_0-output-document-Document|json",
                                 "name": "document",
                                 "label": "Document",
-                                "type": "Document"
+                                "type": "Document | json"
                             },
                             {
                                 "id": "textFile_0-output-text-string|json",
@@ -649,11 +651,11 @@
         },
         {
             "source": "textFile_0",
-            "sourceHandle": "textFile_0-output-document-Document",
+            "sourceHandle": "textFile_0-output-document-Document|json",
             "target": "faiss_0",
             "targetHandle": "faiss_0-input-document-Document",
             "type": "buttonedge",
-            "id": "textFile_0-textFile_0-output-document-Document-faiss_0-faiss_0-input-document-Document",
+            "id": "textFile_0-textFile_0-output-document-Document|json-faiss_0-faiss_0-input-document-Document",
             "data": {
                 "label": ""
             }

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

@@ -1,5 +1,7 @@
 {
     "description": "Use long term memory like Zep to differentiate conversations between users with sessionId",
+    "categories": "ChatOpenAI,Conversational Retrieval QA Chain,Zep Memory,Qdrant,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,6 +1,8 @@
 {
     "description": "Upsert multiple files with metadata and filter by it using conversational retrieval QA chain",
+    "categories": "Text File,PDF File,ChatOpenAI,Conversational Retrieval QA Chain,Pinecone,Langchain",
     "badge": "POPULAR",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,
@@ -126,10 +128,10 @@
                         "type": "options",
                         "options": [
                             {
-                                "id": "textFile_0-output-document-Document",
+                                "id": "textFile_0-output-document-Document|json",
                                 "name": "document",
                                 "label": "Document",
-                                "type": "Document"
+                                "type": "Document | json"
                             },
                             {
                                 "id": "textFile_0-output-text-string|json",
@@ -836,11 +838,11 @@
         },
         {
             "source": "textFile_0",
-            "sourceHandle": "textFile_0-output-document-Document",
+            "sourceHandle": "textFile_0-output-document-Document|json",
             "target": "pinecone_0",
             "targetHandle": "pinecone_0-input-document-Document",
             "type": "buttonedge",
-            "id": "textFile_0-textFile_0-output-document-Document-pinecone_0-pinecone_0-input-document-Document",
+            "id": "textFile_0-textFile_0-output-document-Document|json-pinecone_0-pinecone_0-input-document-Document",
             "data": {
                 "label": ""
             }

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

@@ -1,5 +1,7 @@
 {
     "description": "A chain that automatically picks an appropriate prompt from multiple prompts",
+    "categories": "ChatOpenAI,Multi Prompt Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,5 +1,7 @@
 {
     "description": "A chain that automatically picks an appropriate retriever from multiple different vector databases",
+    "categories": "ChatOpenAI,Multi Retrieval QA Chain,Pinecone,Chroma,Supabase,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,5 +1,7 @@
 {
     "description": "Use the agent to choose between multiple different vector databases, with the ability to use other tools",
+    "categories": "Buffer Memory,ChatOpenAI,Chain Tool,Retrieval QA Chain,Redis,Faiss,Conversational Agent,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,
@@ -964,10 +966,10 @@
                         "type": "options",
                         "options": [
                             {
-                                "id": "plainText_0-output-document-Document",
+                                "id": "plainText_0-output-document-Document|json",
                                 "name": "document",
                                 "label": "Document",
-                                "type": "Document"
+                                "type": "Document | json"
                             },
                             {
                                 "id": "plainText_0-output-text-string|json",
@@ -1501,10 +1503,10 @@
                         "type": "options",
                         "options": [
                             {
-                                "id": "plainText_1-output-document-Document",
+                                "id": "plainText_1-output-document-Document|json",
                                 "name": "document",
                                 "label": "Document",
-                                "type": "Document"
+                                "type": "Document | json"
                             },
                             {
                                 "id": "plainText_1-output-text-string|json",
@@ -1721,11 +1723,11 @@
         },
         {
             "source": "plainText_0",
-            "sourceHandle": "plainText_0-output-document-Document",
+            "sourceHandle": "plainText_0-output-document-Document|json",
             "target": "redis_0",
             "targetHandle": "redis_0-input-document-Document",
             "type": "buttonedge",
-            "id": "plainText_0-plainText_0-output-document-Document-redis_0-redis_0-input-document-Document",
+            "id": "plainText_0-plainText_0-output-document-Document|json-redis_0-redis_0-input-document-Document",
             "data": {
                 "label": ""
             }
@@ -1776,11 +1778,11 @@
         },
         {
             "source": "plainText_1",
-            "sourceHandle": "plainText_1-output-document-Document",
+            "sourceHandle": "plainText_1-output-document-Document|json",
             "target": "faiss_0",
             "targetHandle": "faiss_0-input-document-Document",
             "type": "buttonedge",
-            "id": "plainText_1-plainText_1-output-document-Document-faiss_0-faiss_0-input-document-Document",
+            "id": "plainText_1-plainText_1-output-document-Document|json-faiss_0-faiss_0-input-document-Document",
             "data": {
                 "label": ""
             }

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

@@ -1,5 +1,7 @@
 {
     "description": "An agent that uses OpenAI's Function Calling functionality to pick the tool and args to call",
+    "categories": "Buffer Memory,Custom Tool, SerpAPI,OpenAI Function,Calculator Tool,ChatOpenAI,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,5 +1,7 @@
 {
     "description": "OpenAI Assistant that has instructions and can leverage models, tools, and knowledge to respond to user queries",
+    "categories": "Custom Tool, SerpAPI,OpenAI Assistant,Calculator Tool,Langchain",
+    "framework": "Langchain",
     "badge": "NEW",
     "nodes": [
         {

packages/server/marketplaces/chatflows/Prompt Chaining with VectorStore.json

@@ -1,6 +1,8 @@
 {
     "description": "Use chat history to rephrase user question, and answer the rephrased question using retrieved docs from vector store",
+    "categories": "ChatOpenAI,LLM Chain,SingleStore,Langchain",
     "badge": "POPULAR",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

packages/server/marketplaces/chatflows/Prompt Chaining.json

@@ -1,5 +1,7 @@
 {
     "description": "Use output from a chain as prompt for another chain",
+    "categories": "Custom Tool,OpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

packages/server/marketplaces/chatflows/Query Engine.json

@@ -0,0 +1,549 @@
+{
+    "description": "Stateless query engine designed to answer question over your data using LlamaIndex",
+    "categories": "ChatAnthropic,Compact and Refine,Pinecone,LlamaIndex",
+    "badge": "NEW",
+    "framework": "LlamaIndex",
+    "nodes": [
+        {
+            "width": 300,
+            "height": 382,
+            "id": "queryEngine_0",
+            "position": {
+                "x": 1407.9610494306783,
+                "y": 241.12144405808692
+            },
+            "type": "customNode",
+            "data": {
+                "id": "queryEngine_0",
+                "label": "Query Engine",
+                "version": 1,
+                "name": "queryEngine",
+                "type": "QueryEngine",
+                "baseClasses": ["QueryEngine"],
+                "tags": ["LlamaIndex"],
+                "category": "Engine",
+                "description": "Simple query engine built to answer question over your data, without memory",
+                "inputParams": [
+                    {
+                        "label": "Return Source Documents",
+                        "name": "returnSourceDocuments",
+                        "type": "boolean",
+                        "optional": true,
+                        "id": "queryEngine_0-input-returnSourceDocuments-boolean"
+                    }
+                ],
+                "inputAnchors": [
+                    {
+                        "label": "Vector Store Retriever",
+                        "name": "vectorStoreRetriever",
+                        "type": "VectorIndexRetriever",
+                        "id": "queryEngine_0-input-vectorStoreRetriever-VectorIndexRetriever"
+                    },
+                    {
+                        "label": "Response Synthesizer",
+                        "name": "responseSynthesizer",
+                        "type": "ResponseSynthesizer",
+                        "description": "ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. See <a target=\"_blank\" href=\"https://ts.llamaindex.ai/modules/low_level/response_synthesizer\">more</a>",
+                        "optional": true,
+                        "id": "queryEngine_0-input-responseSynthesizer-ResponseSynthesizer"
+                    }
+                ],
+                "inputs": {
+                    "vectorStoreRetriever": "{{pineconeLlamaIndex_0.data.instance}}",
+                    "responseSynthesizer": "{{compactrefineLlamaIndex_0.data.instance}}",
+                    "returnSourceDocuments": true
+                },
+                "outputAnchors": [
+                    {
+                        "id": "queryEngine_0-output-queryEngine-QueryEngine",
+                        "name": "queryEngine",
+                        "label": "QueryEngine",
+                        "type": "QueryEngine"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 1407.9610494306783,
+                "y": 241.12144405808692
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 585,
+            "id": "pineconeLlamaIndex_0",
+            "position": {
+                "x": 977.3886641397302,
+                "y": -261.2253031641797
+            },
+            "type": "customNode",
+            "data": {
+                "id": "pineconeLlamaIndex_0",
+                "label": "Pinecone",
+                "version": 1,
+                "name": "pineconeLlamaIndex",
+                "type": "Pinecone",
+                "baseClasses": ["Pinecone", "VectorIndexRetriever"],
+                "tags": ["LlamaIndex"],
+                "category": "Vector Stores",
+                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["pineconeApi"],
+                        "id": "pineconeLlamaIndex_0-input-credential-credential"
+                    },
+                    {
+                        "label": "Pinecone Index",
+                        "name": "pineconeIndex",
+                        "type": "string",
+                        "id": "pineconeLlamaIndex_0-input-pineconeIndex-string"
+                    },
+                    {
+                        "label": "Pinecone Namespace",
+                        "name": "pineconeNamespace",
+                        "type": "string",
+                        "placeholder": "my-first-namespace",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pineconeLlamaIndex_0-input-pineconeNamespace-string"
+                    },
+                    {
+                        "label": "Pinecone Metadata Filter",
+                        "name": "pineconeMetadataFilter",
+                        "type": "json",
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "pineconeLlamaIndex_0-input-pineconeMetadataFilter-json"
+                    },
+                    {
+                        "label": "Top K",
+                        "name": "topK",
+                        "description": "Number of top results to fetch. Default to 4",
+                        "placeholder": "4",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pineconeLlamaIndex_0-input-topK-number"
+                    }
+                ],
+                "inputAnchors": [
+                    {
+                        "label": "Document",
+                        "name": "document",
+                        "type": "Document",
+                        "list": true,
+                        "optional": true,
+                        "id": "pineconeLlamaIndex_0-input-document-Document"
+                    },
+                    {
+                        "label": "Chat Model",
+                        "name": "model",
+                        "type": "BaseChatModel_LlamaIndex",
+                        "id": "pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex"
+                    },
+                    {
+                        "label": "Embeddings",
+                        "name": "embeddings",
+                        "type": "BaseEmbedding_LlamaIndex",
+                        "id": "pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex"
+                    }
+                ],
+                "inputs": {
+                    "document": "",
+                    "model": "{{chatAnthropic_LlamaIndex_0.data.instance}}",
+                    "embeddings": "{{openAIEmbedding_LlamaIndex_0.data.instance}}",
+                    "pineconeIndex": "",
+                    "pineconeNamespace": "",
+                    "pineconeMetadataFilter": "",
+                    "topK": ""
+                },
+                "outputAnchors": [
+                    {
+                        "name": "output",
+                        "label": "Output",
+                        "type": "options",
+                        "options": [
+                            {
+                                "id": "pineconeLlamaIndex_0-output-retriever-Pinecone|VectorIndexRetriever",
+                                "name": "retriever",
+                                "label": "Pinecone Retriever",
+                                "type": "Pinecone | VectorIndexRetriever"
+                            },
+                            {
+                                "id": "pineconeLlamaIndex_0-output-retriever-Pinecone|VectorStoreIndex",
+                                "name": "vectorStore",
+                                "label": "Pinecone Vector Store Index",
+                                "type": "Pinecone | VectorStoreIndex"
+                            }
+                        ],
+                        "default": "retriever"
+                    }
+                ],
+                "outputs": {
+                    "output": "retriever"
+                },
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 977.3886641397302,
+                "y": -261.2253031641797
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 334,
+            "id": "openAIEmbedding_LlamaIndex_0",
+            "position": {
+                "x": 529.8690713844503,
+                "y": -18.955726653613254
+            },
+            "type": "customNode",
+            "data": {
+                "id": "openAIEmbedding_LlamaIndex_0",
+                "label": "OpenAI Embedding",
+                "version": 1,
+                "name": "openAIEmbedding_LlamaIndex",
+                "type": "OpenAIEmbedding",
+                "baseClasses": ["OpenAIEmbedding", "BaseEmbedding_LlamaIndex", "BaseEmbedding"],
+                "tags": ["LlamaIndex"],
+                "category": "Embeddings",
+                "description": "OpenAI Embedding specific for LlamaIndex",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["openAIApi"],
+                        "id": "openAIEmbedding_LlamaIndex_0-input-credential-credential"
+                    },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "text-embedding-3-large",
+                                "name": "text-embedding-3-large"
+                            },
+                            {
+                                "label": "text-embedding-3-small",
+                                "name": "text-embedding-3-small"
+                            },
+                            {
+                                "label": "text-embedding-ada-002",
+                                "name": "text-embedding-ada-002"
+                            }
+                        ],
+                        "default": "text-embedding-ada-002",
+                        "optional": true,
+                        "id": "openAIEmbedding_LlamaIndex_0-input-modelName-options"
+                    },
+                    {
+                        "label": "Timeout",
+                        "name": "timeout",
+                        "type": "number",
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "openAIEmbedding_LlamaIndex_0-input-timeout-number"
+                    },
+                    {
+                        "label": "BasePath",
+                        "name": "basepath",
+                        "type": "string",
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "openAIEmbedding_LlamaIndex_0-input-basepath-string"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "timeout": "",
+                    "basepath": ""
+                },
+                "outputAnchors": [
+                    {
+                        "id": "openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding",
+                        "name": "openAIEmbedding_LlamaIndex",
+                        "label": "OpenAIEmbedding",
+                        "type": "OpenAIEmbedding | BaseEmbedding_LlamaIndex | BaseEmbedding"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 529.8690713844503,
+                "y": -18.955726653613254
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 749,
+            "id": "compactrefineLlamaIndex_0",
+            "position": {
+                "x": 170.71031618977543,
+                "y": -33.83233752386292
+            },
+            "type": "customNode",
+            "data": {
+                "id": "compactrefineLlamaIndex_0",
+                "label": "Compact and Refine",
+                "version": 1,
+                "name": "compactrefineLlamaIndex",
+                "type": "CompactRefine",
+                "baseClasses": ["CompactRefine", "ResponseSynthesizer"],
+                "tags": ["LlamaIndex"],
+                "category": "Response Synthesizer",
+                "description": "CompactRefine is a slight variation of Refine that first compacts the text chunks into the smallest possible number of chunks.",
+                "inputParams": [
+                    {
+                        "label": "Refine Prompt",
+                        "name": "refinePrompt",
+                        "type": "string",
+                        "rows": 4,
+                        "default": "The original query is as follows: {query}\nWe have provided an existing answer: {existingAnswer}\nWe have the opportunity to refine the existing answer (only if needed) with some more context below.\n------------\n{context}\n------------\nGiven the new context, refine the original answer to better answer the query. If the context isn't useful, return the original answer.\nRefined Answer:",
+                        "warning": "Prompt can contains no variables, or up to 3 variables. Variables must be {existingAnswer}, {context} and {query}",
+                        "optional": true,
+                        "id": "compactrefineLlamaIndex_0-input-refinePrompt-string"
+                    },
+                    {
+                        "label": "Text QA Prompt",
+                        "name": "textQAPrompt",
+                        "type": "string",
+                        "rows": 4,
+                        "default": "Context information is below.\n---------------------\n{context}\n---------------------\nGiven the context information and not prior knowledge, answer the query.\nQuery: {query}\nAnswer:",
+                        "warning": "Prompt can contains no variables, or up to 2 variables. Variables must be {context} and {query}",
+                        "optional": true,
+                        "id": "compactrefineLlamaIndex_0-input-textQAPrompt-string"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "refinePrompt": "The original query is as follows: {query}\nWe have provided an existing answer: {existingAnswer}\nWe have the opportunity to refine the existing answer (only if needed) with some more context below.\n------------\n{context}\n------------\nGiven the new context, refine the original answer to better answer the query. If the context isn't useful, return the original answer.\nRefined Answer:",
+                    "textQAPrompt": "Context information:\n<context>\n{context}\n</context>\nGiven the context information and not prior knowledge, answer the query.\nQuery: {query}"
+                },
+                "outputAnchors": [
+                    {
+                        "id": "compactrefineLlamaIndex_0-output-compactrefineLlamaIndex-CompactRefine|ResponseSynthesizer",
+                        "name": "compactrefineLlamaIndex",
+                        "label": "CompactRefine",
+                        "type": "CompactRefine | ResponseSynthesizer"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 170.71031618977543,
+                "y": -33.83233752386292
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 529,
+            "id": "chatAnthropic_LlamaIndex_0",
+            "position": {
+                "x": 521.3530883359147,
+                "y": -584.8241219614786
+            },
+            "type": "customNode",
+            "data": {
+                "id": "chatAnthropic_LlamaIndex_0",
+                "label": "ChatAnthropic",
+                "version": 1,
+                "name": "chatAnthropic_LlamaIndex",
+                "type": "ChatAnthropic",
+                "baseClasses": ["ChatAnthropic", "BaseChatModel_LlamaIndex"],
+                "tags": ["LlamaIndex"],
+                "category": "Chat Models",
+                "description": "Wrapper around ChatAnthropic LLM specific for LlamaIndex",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["anthropicApi"],
+                        "id": "chatAnthropic_LlamaIndex_0-input-credential-credential"
+                    },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "claude-2",
+                                "name": "claude-2",
+                                "description": "Claude 2 latest major version, automatically get updates to the model as they are released"
+                            },
+                            {
+                                "label": "claude-2.1",
+                                "name": "claude-2.1",
+                                "description": "Claude 2 latest full version"
+                            },
+                            {
+                                "label": "claude-instant-1",
+                                "name": "claude-instant-1",
+                                "description": "Claude Instant latest major version, automatically get updates to the model as they are released"
+                            },
+                            {
+                                "label": "claude-v1",
+                                "name": "claude-v1"
+                            },
+                            {
+                                "label": "claude-v1-100k",
+                                "name": "claude-v1-100k"
+                            },
+                            {
+                                "label": "claude-v1.0",
+                                "name": "claude-v1.0"
+                            },
+                            {
+                                "label": "claude-v1.2",
+                                "name": "claude-v1.2"
+                            },
+                            {
+                                "label": "claude-v1.3",
+                                "name": "claude-v1.3"
+                            },
+                            {
+                                "label": "claude-v1.3-100k",
+                                "name": "claude-v1.3-100k"
+                            },
+                            {
+                                "label": "claude-instant-v1",
+                                "name": "claude-instant-v1"
+                            },
+                            {
+                                "label": "claude-instant-v1-100k",
+                                "name": "claude-instant-v1-100k"
+                            },
+                            {
+                                "label": "claude-instant-v1.0",
+                                "name": "claude-instant-v1.0"
+                            },
+                            {
+                                "label": "claude-instant-v1.1",
+                                "name": "claude-instant-v1.1"
+                            },
+                            {
+                                "label": "claude-instant-v1.1-100k",
+                                "name": "claude-instant-v1.1-100k"
+                            }
+                        ],
+                        "default": "claude-2",
+                        "optional": true,
+                        "id": "chatAnthropic_LlamaIndex_0-input-modelName-options"
+                    },
+                    {
+                        "label": "Temperature",
+                        "name": "temperature",
+                        "type": "number",
+                        "step": 0.1,
+                        "default": 0.9,
+                        "optional": true,
+                        "id": "chatAnthropic_LlamaIndex_0-input-temperature-number"
+                    },
+                    {
+                        "label": "Max Tokens",
+                        "name": "maxTokensToSample",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatAnthropic_LlamaIndex_0-input-maxTokensToSample-number"
+                    },
+                    {
+                        "label": "Top P",
+                        "name": "topP",
+                        "type": "number",
+                        "step": 0.1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "chatAnthropic_LlamaIndex_0-input-topP-number"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "modelName": "claude-2",
+                    "temperature": 0.9,
+                    "maxTokensToSample": "",
+                    "topP": ""
+                },
+                "outputAnchors": [
+                    {
+                        "id": "chatAnthropic_LlamaIndex_0-output-chatAnthropic_LlamaIndex-ChatAnthropic|BaseChatModel_LlamaIndex",
+                        "name": "chatAnthropic_LlamaIndex",
+                        "label": "ChatAnthropic",
+                        "type": "ChatAnthropic | BaseChatModel_LlamaIndex"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 521.3530883359147,
+                "y": -584.8241219614786
+            },
+            "dragging": false
+        }
+    ],
+    "edges": [
+        {
+            "source": "pineconeLlamaIndex_0",
+            "sourceHandle": "pineconeLlamaIndex_0-output-pineconeLlamaIndex-Pinecone|VectorIndexRetriever",
+            "target": "queryEngine_0",
+            "targetHandle": "queryEngine_0-input-vectorStoreRetriever-VectorIndexRetriever",
+            "type": "buttonedge",
+            "id": "pineconeLlamaIndex_0-pineconeLlamaIndex_0-output-pineconeLlamaIndex-Pinecone|VectorIndexRetriever-queryEngine_0-queryEngine_0-input-vectorStoreRetriever-VectorIndexRetriever",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "openAIEmbedding_LlamaIndex_0",
+            "sourceHandle": "openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding",
+            "target": "pineconeLlamaIndex_0",
+            "targetHandle": "pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex",
+            "type": "buttonedge",
+            "id": "openAIEmbedding_LlamaIndex_0-openAIEmbedding_LlamaIndex_0-output-openAIEmbedding_LlamaIndex-OpenAIEmbedding|BaseEmbedding_LlamaIndex|BaseEmbedding-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-embeddings-BaseEmbedding_LlamaIndex",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "compactrefineLlamaIndex_0",
+            "sourceHandle": "compactrefineLlamaIndex_0-output-compactrefineLlamaIndex-CompactRefine|ResponseSynthesizer",
+            "target": "queryEngine_0",
+            "targetHandle": "queryEngine_0-input-responseSynthesizer-ResponseSynthesizer",
+            "type": "buttonedge",
+            "id": "compactrefineLlamaIndex_0-compactrefineLlamaIndex_0-output-compactrefineLlamaIndex-CompactRefine|ResponseSynthesizer-queryEngine_0-queryEngine_0-input-responseSynthesizer-ResponseSynthesizer",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "chatAnthropic_LlamaIndex_0",
+            "sourceHandle": "chatAnthropic_LlamaIndex_0-output-chatAnthropic_LlamaIndex-ChatAnthropic|BaseChatModel_LlamaIndex",
+            "target": "pineconeLlamaIndex_0",
+            "targetHandle": "pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex",
+            "type": "buttonedge",
+            "id": "chatAnthropic_LlamaIndex_0-chatAnthropic_LlamaIndex_0-output-chatAnthropic_LlamaIndex-ChatAnthropic|BaseChatModel_LlamaIndex-pineconeLlamaIndex_0-pineconeLlamaIndex_0-input-model-BaseChatModel_LlamaIndex",
+            "data": {
+                "label": ""
+            }
+        }
+    ]
+}

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

@@ -1,5 +1,7 @@
 {
     "description": "An agent that uses ReAct logic to decide what action to take",
+    "categories": "Calculator Tool,SerpAPI,ChatOpenAI,MRKL Agent,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,5 +1,7 @@
 {
     "description": "Use Replicate API that runs Llama 13b v2 model with LLMChain",
+    "categories": "Replicate,LLM Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

packages/server/marketplaces/chatflows/SQL DB Chain.json

@@ -1,5 +1,7 @@
 {
     "description": "Answer questions over a SQL database",
+    "categories": "ChatOpenAI,Sql Database Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

packages/server/marketplaces/chatflows/SQL Prompt.json

@@ -1,5 +1,7 @@
 {
     "description": "Manually construct prompts to query a SQL database",
+    "categories": "IfElse Function,Variable Set/Get,Custom JS Function,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
     "badge": "new",
     "nodes": [
         {

packages/server/marketplaces/chatflows/Simple Chat Engine.json

@@ -0,0 +1,272 @@
+{
+    "description": "Simple chat engine to handle back and forth conversations using LlamaIndex",
+    "categories": "BufferMemory,AzureChatOpenAI,LlamaIndex",
+    "framework": "LlamaIndex",
+    "badge": "NEW",
+    "nodes": [
+        {
+            "width": 300,
+            "height": 462,
+            "id": "simpleChatEngine_0",
+            "position": {
+                "x": 1210.127368000538,
+                "y": 324.98110560103896
+            },
+            "type": "customNode",
+            "data": {
+                "id": "simpleChatEngine_0",
+                "label": "Simple Chat Engine",
+                "version": 1,
+                "name": "simpleChatEngine",
+                "type": "SimpleChatEngine",
+                "baseClasses": ["SimpleChatEngine"],
+                "tags": ["LlamaIndex"],
+                "category": "Engine",
+                "description": "Simple engine to handle back and forth conversations",
+                "inputParams": [
+                    {
+                        "label": "System Message",
+                        "name": "systemMessagePrompt",
+                        "type": "string",
+                        "rows": 4,
+                        "optional": true,
+                        "placeholder": "You are a helpful assistant",
+                        "id": "simpleChatEngine_0-input-systemMessagePrompt-string"
+                    }
+                ],
+                "inputAnchors": [
+                    {
+                        "label": "Chat Model",
+                        "name": "model",
+                        "type": "BaseChatModel_LlamaIndex",
+                        "id": "simpleChatEngine_0-input-model-BaseChatModel_LlamaIndex"
+                    },
+                    {
+                        "label": "Memory",
+                        "name": "memory",
+                        "type": "BaseChatMemory",
+                        "id": "simpleChatEngine_0-input-memory-BaseChatMemory"
+                    }
+                ],
+                "inputs": {
+                    "model": "{{azureChatOpenAI_LlamaIndex_0.data.instance}}",
+                    "memory": "{{bufferMemory_0.data.instance}}",
+                    "systemMessagePrompt": "You are a helpful assistant."
+                },
+                "outputAnchors": [
+                    {
+                        "id": "simpleChatEngine_0-output-simpleChatEngine-SimpleChatEngine",
+                        "name": "simpleChatEngine",
+                        "label": "SimpleChatEngine",
+                        "type": "SimpleChatEngine"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "dragging": false,
+            "positionAbsolute": {
+                "x": 1210.127368000538,
+                "y": 324.98110560103896
+            }
+        },
+        {
+            "width": 300,
+            "height": 376,
+            "id": "bufferMemory_0",
+            "position": {
+                "x": 393.9823478014782,
+                "y": 415.7414943210391
+            },
+            "type": "customNode",
+            "data": {
+                "id": "bufferMemory_0",
+                "label": "Buffer Memory",
+                "version": 1,
+                "name": "bufferMemory",
+                "type": "BufferMemory",
+                "baseClasses": ["BufferMemory", "BaseChatMemory", "BaseMemory"],
+                "category": "Memory",
+                "description": "Remembers previous conversational back and forths directly",
+                "inputParams": [
+                    {
+                        "label": "Memory Key",
+                        "name": "memoryKey",
+                        "type": "string",
+                        "default": "chat_history",
+                        "id": "bufferMemory_0-input-memoryKey-string"
+                    },
+                    {
+                        "label": "Input Key",
+                        "name": "inputKey",
+                        "type": "string",
+                        "default": "input",
+                        "id": "bufferMemory_0-input-inputKey-string"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "memoryKey": "chat_history",
+                    "inputKey": "input"
+                },
+                "outputAnchors": [
+                    {
+                        "id": "bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory",
+                        "name": "bufferMemory",
+                        "label": "BufferMemory",
+                        "type": "BufferMemory | BaseChatMemory | BaseMemory"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 393.9823478014782,
+                "y": 415.7414943210391
+            },
+            "dragging": false
+        },
+        {
+            "width": 300,
+            "height": 529,
+            "id": "azureChatOpenAI_LlamaIndex_0",
+            "position": {
+                "x": 746.5530862509605,
+                "y": -54.107978373323306
+            },
+            "type": "customNode",
+            "data": {
+                "id": "azureChatOpenAI_LlamaIndex_0",
+                "label": "AzureChatOpenAI",
+                "version": 1,
+                "name": "azureChatOpenAI_LlamaIndex",
+                "type": "AzureChatOpenAI",
+                "baseClasses": ["AzureChatOpenAI", "BaseChatModel_LlamaIndex"],
+                "tags": ["LlamaIndex"],
+                "category": "Chat Models",
+                "description": "Wrapper around Azure OpenAI Chat LLM specific for LlamaIndex",
+                "inputParams": [
+                    {
+                        "label": "Connect Credential",
+                        "name": "credential",
+                        "type": "credential",
+                        "credentialNames": ["azureOpenAIApi"],
+                        "id": "azureChatOpenAI_LlamaIndex_0-input-credential-credential"
+                    },
+                    {
+                        "label": "Model Name",
+                        "name": "modelName",
+                        "type": "options",
+                        "options": [
+                            {
+                                "label": "gpt-4",
+                                "name": "gpt-4"
+                            },
+                            {
+                                "label": "gpt-4-32k",
+                                "name": "gpt-4-32k"
+                            },
+                            {
+                                "label": "gpt-3.5-turbo",
+                                "name": "gpt-3.5-turbo"
+                            },
+                            {
+                                "label": "gpt-3.5-turbo-16k",
+                                "name": "gpt-3.5-turbo-16k"
+                            }
+                        ],
+                        "default": "gpt-3.5-turbo-16k",
+                        "optional": true,
+                        "id": "azureChatOpenAI_LlamaIndex_0-input-modelName-options"
+                    },
+                    {
+                        "label": "Temperature",
+                        "name": "temperature",
+                        "type": "number",
+                        "step": 0.1,
+                        "default": 0.9,
+                        "optional": true,
+                        "id": "azureChatOpenAI_LlamaIndex_0-input-temperature-number"
+                    },
+                    {
+                        "label": "Max Tokens",
+                        "name": "maxTokens",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "azureChatOpenAI_LlamaIndex_0-input-maxTokens-number"
+                    },
+                    {
+                        "label": "Top Probability",
+                        "name": "topP",
+                        "type": "number",
+                        "step": 0.1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "azureChatOpenAI_LlamaIndex_0-input-topP-number"
+                    },
+                    {
+                        "label": "Timeout",
+                        "name": "timeout",
+                        "type": "number",
+                        "step": 1,
+                        "optional": true,
+                        "additionalParams": true,
+                        "id": "azureChatOpenAI_LlamaIndex_0-input-timeout-number"
+                    }
+                ],
+                "inputAnchors": [],
+                "inputs": {
+                    "modelName": "gpt-3.5-turbo-16k",
+                    "temperature": 0.9,
+                    "maxTokens": "",
+                    "topP": "",
+                    "timeout": ""
+                },
+                "outputAnchors": [
+                    {
+                        "id": "azureChatOpenAI_LlamaIndex_0-output-azureChatOpenAI_LlamaIndex-AzureChatOpenAI|BaseChatModel_LlamaIndex",
+                        "name": "azureChatOpenAI_LlamaIndex",
+                        "label": "AzureChatOpenAI",
+                        "type": "AzureChatOpenAI | BaseChatModel_LlamaIndex"
+                    }
+                ],
+                "outputs": {},
+                "selected": false
+            },
+            "selected": false,
+            "positionAbsolute": {
+                "x": 746.5530862509605,
+                "y": -54.107978373323306
+            },
+            "dragging": false
+        }
+    ],
+    "edges": [
+        {
+            "source": "bufferMemory_0",
+            "sourceHandle": "bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory",
+            "target": "simpleChatEngine_0",
+            "targetHandle": "simpleChatEngine_0-input-memory-BaseChatMemory",
+            "type": "buttonedge",
+            "id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-simpleChatEngine_0-simpleChatEngine_0-input-memory-BaseChatMemory",
+            "data": {
+                "label": ""
+            }
+        },
+        {
+            "source": "azureChatOpenAI_LlamaIndex_0",
+            "sourceHandle": "azureChatOpenAI_LlamaIndex_0-output-azureChatOpenAI_LlamaIndex-AzureChatOpenAI|BaseChatModel_LlamaIndex",
+            "target": "simpleChatEngine_0",
+            "targetHandle": "simpleChatEngine_0-input-model-BaseChatModel_LlamaIndex",
+            "type": "buttonedge",
+            "id": "azureChatOpenAI_LlamaIndex_0-azureChatOpenAI_LlamaIndex_0-output-azureChatOpenAI_LlamaIndex-AzureChatOpenAI|BaseChatModel_LlamaIndex-simpleChatEngine_0-simpleChatEngine_0-input-model-BaseChatModel_LlamaIndex",
+            "data": {
+                "label": ""
+            }
+        }
+    ]
+}

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

@@ -1,5 +1,7 @@
 {
     "description": "Basic example of Conversation Chain with built-in memory - works exactly like ChatGPT",
+    "categories": "Buffer Memory,ChatOpenAI,Conversation Chain,Langchain",
+    "framework": "Langchain",
     "badge": "POPULAR",
     "nodes": [
         {

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

@@ -1,5 +1,7 @@
 {
     "description": "Basic example of stateless (no memory) LLM Chain with a Prompt Template and LLM Model",
+    "categories": "OpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,

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

@@ -1,5 +1,7 @@
 {
     "description": "Return response as a specified JSON structure instead of a string/text",
+    "categories": "Structured Output Parser,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
     "badge": "NEW",
     "nodes": [
         {

packages/server/marketplaces/chatflows/Translator.json

@@ -1,5 +1,7 @@
 {
     "description": "Language translation using LLM Chain with a Chat Prompt Template and Chat Model",
+    "categories": "Chat Prompt Template,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
     "nodes": [
         {
             "width": 300,