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Merge branch 'main' into chore/Upgrade-LC-version 

# Conflicts:
#	packages/components/package.json
This commit is contained in:
Henry 2024-02-07 02:01:32 +08:00
parent 02fe500f21 188e2c748c
commit 73112ad122
54 changed files with 5331 additions and 139 deletions
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2
.github/workflows/autoSyncMergedPullRequest.yml vendored
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@ -3,7 +3,7 @@ on:
pull_request_target:
types:
- closed
branches: [ "main" ]
branches: ['main']
jobs:
autoSyncMergedPullRequest:
if: github.event.pull_request.merged == true

2
package.json
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@ -1,6 +1,6 @@
{
"name": "flowise",
"version": "1.4.11",
"version": "1.5.0",
"private": true,
"homepage": "https://flowiseai.com",
"workspaces": [

135
packages/components/nodes/chatmodels/AzureChatOpenAI/AzureChatOpenAI_LlamaIndex.ts Normal file
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@ -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 }

104
packages/components/nodes/chatmodels/ChatAnthropic/ChatAnthropic_LlamaIndex.ts Normal file
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@ -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 }

4
packages/components/nodes/chatmodels/ChatOpenAI/ChatOpenAI.ts
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@ -79,6 +79,10 @@ class ChatOpenAI_ChatModels implements INode {
label: 'gpt-3.5-turbo',
name: 'gpt-3.5-turbo'
},
{
label: 'gpt-3.5-turbo-0125',
name: 'gpt-3.5-turbo-0125'
},
{
label: 'gpt-3.5-turbo-1106',
name: 'gpt-3.5-turbo-1106'

156
packages/components/nodes/chatmodels/ChatOpenAI/ChatOpenAI_LlamaIndex.ts Normal file
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@ -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 }

16
packages/components/nodes/documentloaders/Folder/Folder.ts
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@ -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',
@ -54,8 +60,11 @@ 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, {
const loader = new DirectoryLoader(
folderPath,
{
'.json': (path) => new JSONLoader(path),
'.txt': (path) => new TextLoader(path),
'.csv': (path) => new CSVLoader(path),
@ -70,6 +79,7 @@ class Folder_DocumentLoaders implements INode {
'.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
@ -95,7 +105,9 @@ class Folder_DocumentLoaders implements INode {
'.html': (path) => new TextLoader(path), // HTML
'.vb': (path) => new TextLoader(path), // Visual Basic
'.xml': (path) => new TextLoader(path) // XML
})
},
recursive
)
let docs = []
if (textSplitter) {

2
packages/components/nodes/documentloaders/VectorStoreToDocument/VectorStoreToDocument.ts
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@ -51,7 +51,7 @@ class VectorStoreToDocument_DocumentLoaders implements INode {
{
label: 'Document',
name: 'document',
baseClasses: this.baseClasses
baseClasses: [...this.baseClasses, 'json']
},
{
label: 'Text',

77
packages/components/nodes/embeddings/AzureOpenAIEmbedding/AzureOpenAIEmbedding_LlamaIndex.ts Normal file
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@ -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 }

91
packages/components/nodes/embeddings/OpenAIEmbedding/OpenAIEmbedding_LlamaIndex.ts Normal file
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@ -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 }

149
packages/components/nodes/engine/ChatEngine/ContextChatEngine.ts Normal file
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@ -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 }

124
packages/components/nodes/engine/ChatEngine/SimpleChatEngine.ts Normal file
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@ -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 }

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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
}

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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 }

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packages/components/nodes/engine/SubQuestionQueryEngine/SubQuestionQueryEngine.ts Normal file
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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 }

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<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>
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packages/components/nodes/responsesynthesizer/CompactRefine/CompactRefine.ts Normal file
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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 }

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<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>
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packages/components/nodes/responsesynthesizer/Refine/Refine.ts Normal file
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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 }

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<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>
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packages/components/nodes/responsesynthesizer/SimpleResponseBuilder/SimpleResponseBuilder.ts Normal file
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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 }

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<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>
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packages/components/nodes/responsesynthesizer/TreeSummarize/TreeSummarize.ts Normal file
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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 }

1
packages/components/nodes/responsesynthesizer/TreeSummarize/treesummarize.svg Normal file
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@ -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>
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11
packages/components/nodes/responsesynthesizer/base.ts Normal file
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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
}
}

2
packages/components/nodes/retrievers/CohereRerankRetriever/CohereRerank.ts
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@ -48,7 +48,7 @@ export class CohereRerank extends BaseDocumentCompressor {
doc.metadata.relevance_score = result.relevance_score
finalResults.push(doc)
})
return finalResults
return finalResults.splice(0, this.k)
} catch (error) {
return documents
}

68
packages/components/nodes/tools/QueryEngineTool/QueryEngineTool.ts Normal file
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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 }

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packages/components/nodes/tools/QueryEngineTool/queryEngineTool.svg Normal file
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@ -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>
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383
packages/components/nodes/vectorstores/Pinecone/Pinecone_LlamaIndex.ts Normal file
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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 }

145
packages/components/nodes/vectorstores/SimpleStore/SimpleStore.ts Normal file
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@ -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 }

6
packages/components/nodes/vectorstores/SimpleStore/simplevs.svg Normal file
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@ -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>
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5
packages/components/package.json
View File

@ -1,6 +1,6 @@
{
"name": "flowise-components",
"version": "1.5.2",
"version": "1.6.0",
"description": "Flowiseai Components",
"main": "dist/src/index",
"types": "dist/src/index.d.ts",
@ -63,9 +63,10 @@
"jsonpointer": "^5.0.1",
"langchain": "^0.1.7",
"langfuse": "2.0.2",
"langfuse-langchain": "2.3.3",
"langfuse-langchain": "2.6.0",
"langsmith": "0.0.63",
"linkifyjs": "^4.1.1",
"llamaindex": "^0.0.48",
"lunary": "^0.6.16",
"mammoth": "^1.5.1",
"moment": "^2.29.3",

1
packages/components/src/Interface.ts
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@ -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

22
packages/components/src/utils.ts
View File

@ -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

917
packages/server/marketplaces/chatflows/Context Chat Engine.json Normal file
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@ -0,0 +1,917 @@
{
"description": "Answer question based on retrieved documents (context) with built-in memory to remember conversation using 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",
"name": "document",
"label": "Document",
"type": "Document"
},
{
"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"
},
{
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4
packages/server/marketplaces/chatflows/Prompt Chaining with VectorStore.json
View File

@ -190,10 +190,10 @@
"type": "options",
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"type": "Document | json"
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{
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547
packages/server/marketplaces/chatflows/Query Engine.json Normal file
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@ -0,0 +1,547 @@
{
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270
packages/server/marketplaces/chatflows/Simple Chat Engine.json Normal file
View File

@ -0,0 +1,270 @@
{
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"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": ""
}
}
]
}

1201
packages/server/marketplaces/chatflows/SubQuestion Query Engine.json Normal file
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2
packages/server/package.json
View File

@ -1,6 +1,6 @@
{
"name": "flowise",
"version": "1.4.11",
"version": "1.5.0",
"description": "Flowiseai Server",
"main": "dist/index",
"types": "dist/index.d.ts",

28
packages/server/src/DataSource.ts
View File

@ -40,7 +40,8 @@ export const init = async (): Promise<void> => {
synchronize: false,
migrationsRun: false,
entities: Object.values(entities),
migrations: mysqlMigrations
migrations: mysqlMigrations,
ssl: getDatabaseSSLFromEnv()
})
break
case 'postgres':
@ -51,18 +52,7 @@ export const init = async (): Promise<void> => {
username: process.env.DATABASE_USER,
password: process.env.DATABASE_PASSWORD,
database: process.env.DATABASE_NAME,
...(process.env.DATABASE_SSL_KEY_BASE64
? {
ssl: {
rejectUnauthorized: false,
cert: Buffer.from(process.env.DATABASE_SSL_KEY_BASE64, 'base64')
}
}
: process.env.DATABASE_SSL === 'true'
? {
ssl: true
}
: {}),
ssl: getDatabaseSSLFromEnv(),
synchronize: false,
migrationsRun: false,
entities: Object.values(entities),
@ -89,3 +79,15 @@ export function getDataSource(): DataSource {
}
return appDataSource
}
const getDatabaseSSLFromEnv = () => {
if (process.env.DATABASE_SSL_KEY_BASE64) {
return {
rejectUnauthorized: false,
ca: Buffer.from(process.env.DATABASE_SSL_KEY_BASE64, 'base64')
}
} else if (process.env.DATABASE_SSL === 'true') {
return true
}
return undefined
}

16
packages/server/src/index.ts
View File

@ -482,7 +482,12 @@ export class App {
const isEndingNode = endingNodeData?.outputs?.output === 'EndingNode'
if (!isEndingNode) {
if (endingNodeData && endingNodeData.category !== 'Chains' && endingNodeData.category !== 'Agents') {
if (
endingNodeData &&
endingNodeData.category !== 'Chains' &&
endingNodeData.category !== 'Agents' &&
endingNodeData.category !== 'Engine'
) {
return res.status(500).send(`Ending node must be either a Chain or Agent`)
}
}
@ -1440,7 +1445,7 @@ export class App {
chatType,
chatId,
memoryType: memoryType ?? (chatId ? IsNull() : undefined),
sessionId: sessionId ?? (chatId ? IsNull() : undefined),
sessionId: sessionId ?? undefined,
createdDate: toDate && fromDate ? Between(fromDate, toDate) : undefined
},
order: {
@ -1690,7 +1695,12 @@ export class App {
const isEndingNode = endingNodeData?.outputs?.output === 'EndingNode'
if (!isEndingNode) {
if (endingNodeData && endingNodeData.category !== 'Chains' && endingNodeData.category !== 'Agents') {
if (
endingNodeData &&
endingNodeData.category !== 'Chains' &&
endingNodeData.category !== 'Agents' &&
endingNodeData.category !== 'Engine'
) {
return res.status(500).send(`Ending node must be either a Chain or Agent`)
}

14
packages/server/src/utils/index.ts
View File

@ -818,7 +818,16 @@ export const findAvailableConfigs = (reactFlowNodes: IReactFlowNode[], component
*/
export const isFlowValidForStream = (reactFlowNodes: IReactFlowNode[], endingNodeData: INodeData) => {
const streamAvailableLLMs = {
'Chat Models': ['azureChatOpenAI', 'chatOpenAI', 'chatAnthropic', 'chatOllama', 'awsChatBedrock', 'chatMistralAI'],
'Chat Models': [
'azureChatOpenAI',
'chatOpenAI',
'chatOpenAI_LlamaIndex',
'chatAnthropic',
'chatAnthropic_LlamaIndex',
'chatOllama',
'awsChatBedrock',
'chatMistralAI'
],
LLMs: ['azureOpenAI', 'openAI', 'ollama']
}
@ -841,6 +850,9 @@ export const isFlowValidForStream = (reactFlowNodes: IReactFlowNode[], endingNod
// Agent that are available to stream
const whitelistAgents = ['openAIFunctionAgent', 'csvAgent', 'airtableAgent', 'conversationalRetrievalAgent']
isValidChainOrAgent = whitelistAgents.includes(endingNodeData.name)
} else if (endingNodeData.category === 'Engine') {
const whitelistEngine = ['contextChatEngine', 'simpleChatEngine', 'queryEngine', 'subQuestionQueryEngine']
isValidChainOrAgent = whitelistEngine.includes(endingNodeData.name)
}
// If no output parser, flow is available to stream

2
packages/ui/package.json
View File

@ -1,6 +1,6 @@
{
"name": "flowise-ui",
"version": "1.4.8",
"version": "1.5.0",
"license": "SEE LICENSE IN LICENSE.md",
"homepage": "https://flowiseai.com",
"author": {

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29
packages/ui/src/ui-component/dialog/NodeInfoDialog.js
View File

@ -132,6 +132,35 @@ const NodeInfoDialog = ({ show, dialogProps, onCancel }) => {
</span>
</div>
)}
{dialogProps.data.tags &&
dialogProps.data.tags.length &&
dialogProps.data.tags.map((tag, index) => (
<div
style={{
display: 'flex',
flexDirection: 'row',
width: 'max-content',
borderRadius: 15,
background: '#cae9ff',
padding: 5,
paddingLeft: 10,
paddingRight: 10,
marginTop: 5,
marginLeft: 10,
marginBottom: 5
}}
key={index}
>
<span
style={{
color: '#023e7d',
fontSize: '0.825rem'
}}
>
{tag.toLowerCase()}
</span>
</div>
))}
</div>
</div>
</div>

1
packages/ui/src/utils/genericHelper.js
View File

@ -277,6 +277,7 @@ export const generateExportFlowData = (flowData) => {
name: node.data.name,
type: node.data.type,
baseClasses: node.data.baseClasses,
tags: node.data.tags,
category: node.data.category,
description: node.data.description,
inputParams: node.data.inputParams,

100
packages/ui/src/views/canvas/AddNodes.js
View File

@ -22,7 +22,9 @@ import {
Popper,
Stack,
Typography,
Chip
Chip,
Tab,
Tabs
} from '@mui/material'
import ExpandMoreIcon from '@mui/icons-material/ExpandMore'
@ -36,12 +38,20 @@ import { StyledFab } from 'ui-component/button/StyledFab'
// icons
import { IconPlus, IconSearch, IconMinus, IconX } from '@tabler/icons'
import LlamaindexPNG from 'assets/images/llamaindex.png'
import LangChainPNG from 'assets/images/langchain.png'
// const
import { baseURL } from 'store/constant'
import { SET_COMPONENT_NODES } from 'store/actions'
// ==============================|| ADD NODES||============================== //
function a11yProps(index) {
return {
id: `attachment-tab-${index}`,
'aria-controls': `attachment-tabpanel-${index}`
}
}
const AddNodes = ({ nodesData, node }) => {
const theme = useTheme()
@ -52,6 +62,7 @@ const AddNodes = ({ nodesData, node }) => {
const [nodes, setNodes] = useState({})
const [open, setOpen] = useState(false)
const [categoryExpanded, setCategoryExpanded] = useState({})
const [tabValue, setTabValue] = useState(0)
const anchorRef = useRef(null)
const prevOpen = useRef(open)
@ -86,6 +97,11 @@ const AddNodes = ({ nodesData, node }) => {
}
}
const handleTabChange = (event, newValue) => {
setTabValue(newValue)
filterSearch(searchValue, newValue)
}
const getSearchedNodes = (value) => {
const passed = nodesData.filter((nd) => {
const passesQuery = nd.name.toLowerCase().includes(value.toLowerCase())
@ -95,23 +111,34 @@ const AddNodes = ({ nodesData, node }) => {
return passed
}
const filterSearch = (value) => {
const filterSearch = (value, newTabValue) => {
setSearchValue(value)
setTimeout(() => {
if (value) {
const returnData = getSearchedNodes(value)
groupByCategory(returnData, true)
groupByCategory(returnData, newTabValue ?? tabValue, true)
scrollTop()
} else if (value === '') {
groupByCategory(nodesData)
groupByCategory(nodesData, newTabValue ?? tabValue)
scrollTop()
}
}, 500)
}
const groupByCategory = (nodes, isFilter) => {
const groupByTags = (nodes, newTabValue = 0) => {
const langchainNodes = nodes.filter((nd) => !nd.tags)
const llmaindexNodes = nodes.filter((nd) => nd.tags && nd.tags.includes('LlamaIndex'))
if (newTabValue === 0) {
return langchainNodes
} else {
return llmaindexNodes
}
}
const groupByCategory = (nodes, newTabValue, isFilter) => {
const taggedNodes = groupByTags(nodes, newTabValue)
const accordianCategories = {}
const result = nodes.reduce(function (r, a) {
const result = taggedNodes.reduce(function (r, a) {
r[a.category] = r[a.category] || []
r[a.category].push(a)
accordianCategories[a.category] = isFilter ? true : false
@ -244,15 +271,72 @@ const AddNodes = ({ nodesData, node }) => {
'aria-label': 'weight'
}}
/>
<Tabs
sx={{ position: 'relative', minHeight: '50px', height: '50px' }}
variant='fullWidth'
value={tabValue}
onChange={handleTabChange}
aria-label='tabs'
>
{['LangChain', 'LlamaIndex'].map((item, index) => (
<Tab
icon={
<div
style={{
borderRadius: '50%'
}}
>
<img
style={{
width: '25px',
height: '25px',
borderRadius: '50%',
objectFit: 'contain'
}}
src={index === 0 ? LangChainPNG : LlamaindexPNG}
alt={item}
/>
</div>
}
iconPosition='start'
sx={{ minHeight: '50px', height: '50px' }}
key={index}
label={item}
{...a11yProps(index)}
></Tab>
))}
<div
style={{
display: 'flex',
flexDirection: 'row',
alignItems: 'center',
borderRadius: 10,
background: 'rgb(254,252,191)',
paddingLeft: 6,
paddingRight: 6,
paddingTop: 1,
paddingBottom: 1,
width: 'max-content',
position: 'absolute',
top: 0,
right: 0,
fontSize: '0.65rem',
fontWeight: 700
}}
>
<span style={{ color: 'rgb(116,66,16)' }}>BETA</span>
</div>
</Tabs>
<Divider />
</Box>
<PerfectScrollbar
containerRef={(el) => {
ps.current = el
}}
style={{ height: '100%', maxHeight: 'calc(100vh - 320px)', overflowX: 'hidden' }}
style={{ height: '100%', maxHeight: 'calc(100vh - 380px)', overflowX: 'hidden' }}
>
<Box sx={{ p: 2 }}>
<Box sx={{ p: 2, pt: 0 }}>
<List
sx={{
width: '100%',

19
packages/ui/src/views/canvas/CanvasNode.js
View File

@ -19,6 +19,7 @@ import NodeInfoDialog from 'ui-component/dialog/NodeInfoDialog'
import { baseURL } from 'store/constant'
import { IconTrash, IconCopy, IconInfoCircle, IconAlertTriangle } from '@tabler/icons'
import { flowContext } from 'store/context/ReactFlowContext'
import LlamaindexPNG from 'assets/images/llamaindex.png'
// ===========================|| CANVAS NODE ||=========================== //
@ -158,9 +159,25 @@ const CanvasNode = ({ data }) => {
{data.label}
</Typography>
</Box>
<div style={{ flexGrow: 1 }}></div>
{data.tags && data.tags.includes('LlamaIndex') && (
<>
<div
style={{
borderRadius: '50%',
padding: 15
}}
>
<img
style={{ width: '25px', height: '25px', borderRadius: '50%', objectFit: 'contain' }}
src={LlamaindexPNG}
alt='LlamaIndex'
/>
</div>
</>
)}
{warningMessage && (
<>
<div style={{ flexGrow: 1 }}></div>
<Tooltip title={<span style={{ whiteSpace: 'pre-line' }}>{warningMessage}</span>} placement='top'>
<IconButton sx={{ height: 35, width: 35 }}>
<IconAlertTriangle size={35} color='orange' />

18
packages/ui/src/views/marketplaces/MarketplaceCanvasNode.js
View File

@ -13,6 +13,7 @@ import AdditionalParamsDialog from 'ui-component/dialog/AdditionalParamsDialog'
// const
import { baseURL } from 'store/constant'
import LlamaindexPNG from 'assets/images/llamaindex.png'
const CardWrapper = styled(MainCard)(({ theme }) => ({
background: theme.palette.card.main,
@ -87,6 +88,23 @@ const MarketplaceCanvasNode = ({ data }) => {
{data.label}
</Typography>
</Box>
<div style={{ flexGrow: 1 }}></div>
{data.tags && data.tags.includes('LlamaIndex') && (
<>
<div
style={{
borderRadius: '50%',
padding: 15
}}
>
<img
style={{ width: '25px', height: '25px', borderRadius: '50%', objectFit: 'contain' }}
src={LlamaindexPNG}
alt='LlamaIndex'
/>
</div>
</>
)}
</div>
{(data.inputAnchors.length > 0 || data.inputParams.length > 0) && (
<>